ALBERT

Description: In recent years data acquisition from remote sensing has become readily available to the quarry sector. This study demonstrates how such data may be used to evaluate and back analyse rockfall potential of a legacy slope in a blocky rock mass. Use of data obtained from several aerial LiDAR (Light Detection and Ranging) and photogrammetric campaigns taken over a number of years (2011 to date) provides evidence for potential rockfall evolution from a slope within an active quarry operation in Cornwall, UK. Further investigation, through analysis of point cloud data obtained from terrestrial laser scanning, was undertaken to characterise the orientation of discontinuities present within the rock slope. Aerial and terrestrial LiDAR data were subsequently used for kinematic analysis, production of surface topography models and rockfall trajectory analyses using both 2D and 3D numerical simulations. The results of an Unmanned Aerial Vehicle (UAV)-based 3D photogrammetric analysis enabled the reconstruction of high resolution topography, allowing one to not only determine geometrical properties of the slope surface and geo-mechanical characterisation but provide data for validation of numerical simulations. The analysis undertaken shows the effectiveness of the existing rockfall barrier, while demonstrating how photogrammetric data can be used to inform back analyses of the underlying failure mechanism and investigate potential runout.

Description: The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling.

Description: This study aims to improve the implementation of models of geospatial information in Web Ontology Language (OWL). Large amounts of geospatial information are maintained in Geographic Information Systems (GIS) based on models according to the Unified Modeling Language (UML) and standards from ISO/TC 211 and the Open Geospatial Consortium (OGC). Sharing models and geospatial information in the Semantic Web will increase the usability and value of models and information, as well as enable linking with spatial and non-spatial information from other domains. Methods for conversion from UML to OWL for basic concepts used in models of geospatial information have been studied and evaluated. Primary conversion challenges have been identified with specific attention to whether adapted rules for UML modelling could contribute to improved conversions. Results indicated that restrictions related to abstract classes, unions, compositions and code lists in UML are challenging in the Open World Assumption (OWA) on which OWL is based. Two conversion challenges are addressed by adding more semantics to UML models: global properties and reuse of external concepts. The proposed solution is formalized in a UML profile supported by rules and recommendations and demonstrated with a UML model based on the Intelligent Transport Systems (ITS) standard ISO 14825 Geographic Data Files (GDF). The scope of the resulting ontology will determine to what degree the restrictions shall be maintained in OWL, and different conversion methods are needed for different scopes.

Description: The 3D road network scene helps to simulate the distribution of road infrastructure and the corresponding traffic conditions. However, the existing road modeling methods have limitations such as inflexibility in different types of road construction, inferior quality in visual effects and poor efficiency for large-scale model rendering. To tackle these challenges, a template-based 3D road modeling method is proposed in this paper. In this method, the road GIS data is first pre-processed before modeling. The road centerlines are analyzed to extract topology information and resampled to improve path accuracy and match the terrain. Meanwhile, the road network is segmented and organized using a hierarchical block data structure. Road elements, including roadbeds, road facilities and moving vehicles are then designed based on templates. These templates define the geometric and semantic information of elements along both the cross-section and road centerline. Finally, the road network scene is built by the construction algorithms, where roads, at-grade intersections, grade separated areas and moving vehicles are modeled and simulated separately. The proposed method is tested by generating large-scale virtual road network scenes in the World Wind, an open source software package. The experimental results demonstrate that the method is flexible and can be used to develop different types of road models and efficiently simulate large-scale road network environments.

Description: In Africa, there is growing knowledge regarding the use of data obtained by remote sensing and analysed while using Geographic Information Systems for solving myriad problems. The awareness has largely arisen through the efforts of the Programme on Space Applications (PSA) of the United Nations Office for Outer Space Affairs (UNOOSA), and the subsequent UN resolutions for the establishment of Regional Centres for Space Science and Technology Education, to train scientists and researchers in different thematic areas of space, including Remote Sensing/Geographic Information Systems (RS/GIS). The African Regional Centre for Space Science and Technology Education in English (ARCSSTE-E) is one of these regional centres. The Centre has successfully trained 474 professionals from 18 countries since its inception in 1998; about 14% of these trainees have been female. This paper highlights the training programmes of ARCSSTE-E from its inception, and discusses the potential areas of improvement with a focus on the RS/GIS area. In 2019, a survey was conducted on alumni of the Postgraduate Diploma (PGD) programme of ARCSSTE-E. Based on the analysis of their responses and the progression of the PGD programme to a new Masters programme in RS/GIS at the university, there is clear evidence regarding the impact of the UNOOSA-assisted capacity building programme on the work and career of alumni, which has already produced an appreciable number of trained personnel in developing countries in Africa.

Description: Conventional Geographical Information Systems (GIS) software struggles to represent uncertain and contested historical knowledge. An ontology, meaning a semantic structure defining named entities, and explicit and typed relationships, can be constructed in the absence of locational data, and spatial objects can be attached to this structure if and when they become available. We describe the overall architecture of the Great Britain Historical GIS, and the PastPlace Administrative Unit Ontology that forms its core. Then, we show how particular historical geographies can be represented within this architecture through two case studies, both emphasizing entity definition and especially the application of a multi-level typology, in which each “unit” has an unchanging “type” but also a time-variant “status”. The first includes the linked systems of Poor Law unions and registration districts in 19th century England and Wales, in which most but not all unions and districts were coterminous. The second case study includes the international system of nation-states, in which most units do not appear from nothing, but rather gain or lose independence. We show that a relatively simple data model is able to represent much historical complexity.

Description: Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user’s preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user’s main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user’s sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user’s main behavioral intention in the user’s check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.

Description: Complex natural disasters often cause people to suffer hardships, and they can cause a large number of casualties. A population that has been affected by a natural disaster is at high risk and desperately in need of help. Even with the timely assessment and knowledge of the degree that natural disasters affect populations, challenges arise during emergency response in the aftermath of a natural disaster. This paper proposes an approach to assessing the near-real-time intensity of the affected population using social media data. Because of its fatal impact on the Philippines, Typhoon Haiyan was selected as a case study. The results show that the normalized affected population index (NAPI) has a significant ability to indicate the affected population intensity. With the geographic information of disasters, more accurate and relevant disaster relief information can be extracted from social media data. The method proposed in this paper will benefit disaster relief operations and decision-making, which can be executed in a timely manner.

Description: This study compares the performance of five popular equal-area projections supported by Free and Open Source Software for Geo-spatial (FOSS4G)—Sinusoidal, Mollweide, Hammer, Eckert IV and Homolosine. A set of 21,872 discrete distortion vindicatrices were positioned on the ellipsoid surface, centred on the cells of a Snyder icosahedral equal-area grid. These indicatrices were projected on the plane and the resulting angular and distance distortions computed, all using FOSS4G. The Homolosine is the only projection that manages to minimise angular and distance distortions simultaneously. It yields the lowest distortions among this set of projections and clearly outclasses when only land masses are considered. These results also indicate the Sinusoidal and Hammer projections to be largely outdated, imposing too large distortions to be useful. In contrast, the Mollweide and Eckert IV projections present trade-offs between visual expression and accuracy that are worth considering. However, for the purposes of storing and analysing big spatial data with FOSS4G the superior performance of the Homolosine projection makes its choice difficult to avoid.

Description: 3D city models are being extensively used in applications such as evacuation scenarios and energy consumption estimation. The main standard for 3D city models is the CityGML data model which can be encoded through the CityJSON data format. CityGML and CityJSON use polygonal modelling in order to represent geometries. True topological data structures have proven to be more computationally efficient for geometric analysis compared to polygonal modelling. In a previous study, we have introduced a method to topologically reconstruct CityGML models while maintaining the semantic information of the dataset, based solely on the combinatorial map (C-Map) data structure. As a result of the limitations of C-Map’s semantic representation mechanism, the resulting datasets could suffer either from semantic information loss or the redundant repetition of them. In this article, we propose a solution for a more efficient representation of geometry, topology and semantics by incorporating the C-Map data structure into the CityGML data model and implementing a CityJSON extension to encode the C-Map data. In addition, we provide an algorithm for the topological reconstruction of CityJSON datasets to append them according to this extension. Finally, we apply our methodology to three open datasets in order to validate our approach when applied to real-world data. Our results show that the proposed CityJSON extension can represent all geometric information of a city model in a lossless way, providing additional topological information for the objects of the model.

Description: We are now generating exponentially more data from more sources than a few years ago. Big data, an already familiar term, has been generally defined as a massive volume of structured, semi-structured, and/or unstructured data, which may not be effectively managed and processed using traditional databases and software techniques. It could be problematic to visualize easily and quickly a large amount of data via an Internet platform. From this perspective, the main aim of the paper is to test point data visualization possibilities of selected JavaScript Mapping Libraries to measure their performance and ability to cope with a big amount of data. Nine datasets containing 10,000 to 3,000,000 points were generated from the Nature Conservation Database. Five libraries for marker clustering and two libraries for heatmap visualization were analyzed. Loading time and the ability to visualize large data sets were compared for each dataset and each library. The best-evaluated library was a Mapbox GL JS (Graphics Library JavaScript) with the highest overall performance. Some of the tested libraries were not able to handle the desired amount of data. In general, an amount of less than 100,000 points was indicated as the threshold for implementation without a noticeable slowdown in performance. Their usage can be a limiting factor for point data visualization in such a dynamic environment as we live nowadays.

Description: Large-scale three-dimensional (3D) reconstruction from multi-view images is used to generate 3D mesh surfaces, which are usually built for urban areas and are widely applied in many research hotspots, such as smart cities. Their simplification is a significant step for 3D roaming, pattern recognition, and other research fields. The simplification quality has been assessed in several studies. On the one hand, almost all studies on surface simplification have measured simplification errors using the surface comparison tool Metro, which does not preserve sufficient detail. On the other hand, the reconstruction precision of urban surfaces varies as a result of homogeneity or heterogeneity. Therefore, it is difficult to assess simplification quality without surface classification. These difficulties are addressed in this study by first classifying urban surfaces into planar surfaces, detailed surfaces, and urban frameworks according to the simplification errors of different types of surfaces and then measuring these errors after sampling. A series of assessment indexes are also provided to contribute to the advancement of simplification algorithms.

Description: Satellite data are underutilized in many branches of operational oceanography. Users outside of the satellite community often encounter difficulty in discovering the types of satellite measurements that are available, and determining which satellite products are best for operational activities. In addition, the large choice of satellite data providers, each with their own data access protocols and formats, can make data access challenging. The mission of the NOAA CoastWatch Program is to make ocean satellite data easier to access and to apply to operational uses. As part of this mission, the West Coast Node of CoastWatch developed the NOAA Ocean Satellite Course, which introduces scientists and resource managers to ocean satellite products, and provides them tools to facilitate data access when using common analysis software. These tools leverage the data services provided by ERDDAP, a data distribution system designed to make data access easier via a graphical user interface and via machine-to-machine connections. The course has been offered annually since 2006 and has been attended by over 350 participants. Results of post-course surveys are analyzed to measure course effectiveness. The lessons learned from conducting these courses include using the preferred software of the course participants, providing easy access to datasets that are appropriate (fit for purpose) for operation applications, developing tools that address common tasks of the target audience, and minimizing the financial barriers to attend the course.

Description: The rental housing market plays a critical role in the United States real estate market. In addition, rent changes are also indicators of urban transformation and social phenomena. However, traditional data sources for market rent prediction are often inaccurate or inadequate at covering large geographies. With the development of housing information exchange platforms such as Craigslist, user-generated rental listings now provide big data that cover wide geographies and are rich in textual information. Given the importance of rent prediction in urban studies, this study aims to develop and evaluate models of rental market dynamics using deep learning approaches on spatial and textual data from Craigslist rental listings. We tested a number of machine learning and deep learning models (e.g., convolutional neural network, recurrent neural network) for the prediction of rental prices based on data collected from Atlanta, GA, USA. With textual information alone, deep learning models achieved an average root mean square error (RMSE) of 288.4 and mean absolute error (MAE) of 196.8. When combining textual information with location and housing attributes, the integrated model achieved an average RMSE of 227.9 and MAE of 145.4. These approaches can be applied to assess the market value of rental properties, and the prediction results can be used as indicators of a variety of urban phenomena and provide practical references for home owners and renters.

Description: Developers have long used game engines for visualizing virtual worlds for players to explore. However, using real-world data in a game engine is always a challenging task, since most game engines have very little support for geospatial data. This paper presents our findings from exploring the Unity3D game engine for visualizing large-scale topographic data from mixed sources of terrestrial laser scanner models and topographic map data. Level of detail (LOD) 3 3D models of two buildings of the Universitas Gadjah Mada campus were obtained using a terrestrial laser scanner converted into the FBX format. Mapbox for Unity was used to provide georeferencing support for the 3D model. Unity3D also used road and place name layers via Mapbox for Unity based on OpenStreetMap (OSM) data. LOD1 buildings were modeled from topographic map data using Mapbox, and 3D models from the terrestrial laser scanner replaced two of these buildings. Building information and attributes, as well as visual appearances, were added to 3D features. The Unity3D game engine provides a rich set of libraries and assets for user interactions, and custom C# scripts were used to provide a bird’s-eye-view mode of 3D zoom, pan, and orbital display. In addition to basic 3D navigation tools, a first-person view of the scene was utilized to enable users to gain a walk-through experience while virtually inspecting the objects on the ground. For a fly-through experience, a drone view was offered to help users inspect objects from the air. The result was a multiplatform 3D visualization capable of displaying 3D models in LOD3, as well as providing user interfaces for exploring the scene using “on the ground” and “from the air” types of first person view interactions. Using the Unity3D game engine to visualize mixed sources of topographic data creates many opportunities to optimize large-scale topographic data use.

Description: Information from social media microblogging has been applied to management of emergency situations following disasters. In particular, such blogs contain much information about the public perception of disasters. However, the effective collection and use of disaster information from microblogs still presents a significant challenge. In this paper, a spatial distribution detection method is established using emergency information based on the urgency degree grading of microblogs and spatial autocorrelation analysis. Moreover, a character-level convolutional neural network classifier is applied for microblog classification in order to mine the spatio-temporal change process of emergency rescue information. The results from the Jiuzhaigou (Sichuan, China) earthquake case study demonstrate that different emergency information types exhibit different time variation characteristics. Moreover, spatial autocorrelation analysis based on the degree of text urgency can exclude uneven spatial distribution influences of the number of microblog users, and accurately determine the level of urgency of the situation. In addition, the classification and spatio-temporal analysis methods combined in this study can effectively mine the required emergency information, allowing us to understand emergency information spatio-temporal changes. Our study can be used as a reference for microblog information applications within the field of emergency rescue activity.

Description: Quantitative assessments and dynamic monitoring of indicators based on fine-scale population data are necessary to support the implementation of the United Nations (UN) 2030 Agenda and to comprehensively achieve its 17 Sustainable Development Goals (SDGs). However, most population data are collected by administrative units, and it is difficult to reflect true distribution and uniformity in space. To solve this problem, based on fine building information, a geospatial disaggregation method of population data for supporting SDG assessments is presented in this paper. First, Deqing County in China, which was divided into residential areas and nonresidential areas according to the idea of dasymetric mapping, was selected as the study area. Then, the town administrative areas were taken as control units, building area and number of floors were used as weighting factors to establish the disaggregation model, and population data with a resolution of 30 m in Deqing County in 2016 were obtained. After analyzing the statistical population of 160 villages and the disaggregation results, we found that the global average accuracy was 87.08%. Finally, by using the disaggregation population data, indicators 3.8.1, 4.a.1, and 9.1.1 were selected to conduct an accessibility analysis and a buffer analysis in a quantitative assessment of the SDGs. The results showed that the SDG measurement and assessment results based on the disaggregated population data were more accurate and effective than the results obtained using the traditional method.

Description: Recent federal documents devoted to the Arctic zone economic development highlighted eight basic areas—future innovative centers of regional development. Totally 150 investment projects are planned by 2030, where 48% are designated for mineral resources extraction, 16%—for transport development, 7%—for geological survey, 2%—for environment safety protection etc. At the same time, these ambitious plans should meet green economy goals. This means that territorial planning will have to consider at least three spatially differentiated issues: Socio-economic, ecological and environmental (nature hazards, climatic changes etc.). Thus, the initial stage of territorial planning for economic development needs evaluation of different spatial combinations of these issues. This research presents an algorithm for evaluation of joint impact of basic regional components, characterizing “nature-population-economy” interrelations in order to reveal their spatial differences and demonstrate options and risks for future sustainable development of the Russian Arctic. Basic research methods included system analysis with GIS tools. Accumulated data were arranged in three blocks which included principle regional factors which control sustainable development. In order to find different patterns of sustainability provided by these factors pair assessments of ecological/economic, environmental/economic and ecological/environmental data was done. Independent variable-environmental factors offered different spatial natural patterns either promoting or hampering economic development. It was impossible to assess jointly all three blocks data because the discussed framework of regional sustainability factors attributed to spatial regional system, which demonstrated its panarchy character. Ranking results were visualized in a map where the selected pair groups were shown for each basic territory of advanced development. Visualization of proportional correlation of social, economic and ecological factors was achieved using color triangle method (RGB).

Description: Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

Description: Ground-truth datasets are essential for the training and evaluation of any automated algorithm. As such, gold-standard annotated corpora underlie most advances in natural language processing (NLP). However, only a few relatively small (geo-)annotated datasets are available for geoparsing, i.e., the automatic recognition and geolocation of place references in unstructured text. The creation of geoparsing corpora that include both the recognition of place names in text and matching of those names to toponyms in a geographic gazetteer (a process we call geo-annotation), is a laborious, time-consuming and expensive task. The field lacks efficient geo-annotation tools to support corpus building and lacks design guidelines for the development of such tools. Here, we present the iterative design of GeoAnnotator, a web-based, semi-automatic and collaborative visual analytics platform for geo-annotation. GeoAnnotator facilitates collaborative, multi-annotator creation of large corpora of geo-annotated text by generating computationally-generated pre-annotations that can be improved by human-annotator users. The resulting corpora can be used in improving and benchmarking geoparsing algorithms as well as various other spatial language-related methods. Further, the iterative design process and the resulting design decisions can be used in annotation platforms tailored for other application domains of NLP.

Description: Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.

Description: Map projections are one of the foundations of geographic information science and cartography. An understanding of the different projection variants and properties is critical when creating maps or carrying out geospatial analyses. The common way of teaching map projections in text books makes use of the light source (or light bulb) metaphor, which draws a comparison between the construction of a map projection and the way light rays travel from the light source to the projection surface. Although conceptually plausible, such explanations were created for the static instructions in textbooks. Modern web technologies may provide a more comprehensive learning experience by allowing the student to interactively explore (in guided or unguided mode) the way map projections can be constructed following the light source metaphor. The implementation of this approach, however, is not trivial as it requires detailed knowledge of map projections and computer graphics. Therefore, this paper describes the underlying computational methods and presents a prototype as an example of how this concept can be applied in practice. The prototype will be integrated into the Geographic Information Technology Training Alliance (GITTA) platform to complement the lesson on map projections.

Description: Taxi demand prediction is one of the key factors in making online taxi hailing services more successful and more popular. Accurate taxi demand prediction can bring various advantages including, but not limited to, enhancing user experience, increasing taxi utilization, and optimizing traffic efficiency. However, the task is challenging because of complex spatial and temporal dependencies of taxi demand. In addition, relationships between non-adjacent regions are also critical for accurate taxi demand prediction, whereas they are largely ignored by existing approaches. To this end, we propose a novel graph and time-series learning model for city-wide taxi demand prediction in this paper. It has two main building blocks, the first one utilize a graph network with attention mechanism to effectively learn spatial dependencies of taxi demand in a broader perspective of the entire city, and the output at each time interval is then transferred to the second block. In the graph network, the edge is defined by an Origin–Destination relation to capture non-adjacent impacts. The second one uses a neural network which is adept with processing sequence data to capture the temporal correlations of city-wide taxi demand. Using a large, real-world dataset and three metrics, we conduct an extensive experimental study and find that our model outperforms state-of-the-art baselines by 9.3% in terms of the root-mean-square error.

Description: Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

Description: To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along network infrastructure simultaneously. In TOST, a flexible intersection-based semantic representation is designed to express movement typically constrained by urban road networks by combining sets of local semantic details along the time axis. A relational schema based on this model was instantiated against real datasets, which illustrated the effectivity of our proposed model.

Description: Building fire is a complex geographic process related to the indoor spatial environment, a smart spatial data model can accurately describe the spatial-temporal information of a building fire scene, which is important for modeling a fire process. With the development of fire dynamics and computer science, many building fire models have been proposed and widely used. However, the spatial representation of these models is relatively weak. In this study, a fire process modeled via the Fire Dynamics Simulator (FDS) and the requirements of a spatial data model are initially analyzed. Then, a new spatial data model named the Combinatorial Spatial Data Model (CSDM) is combined with Geographic Information System (GIS). The key features of the CSDM, which include spatial, semantic, topological, event and state representations of a building fire scene modeled via the CSDM are subsequently presented. In addition, the Unified Modeling Language (UML) class diagram of the CSDM is also presented, and then experiments with a simplified building are conducted as a CSDM implementation case. A method of transferring data from the CSDM to FDS and a building fire analysis approach using the CSDM are subsequently proposed.

Description: Accurate bathymetric modeling is required for safe maritime navigation in shallow waters as well as for other marine operations. Traditionally, bathymetric modeling is commonly carried out using linear models, such as the Stumpf method. Linear methods are developed to derive bathymetry using the strong linear correlation between the grey values of satellite imagery visible bands and the water depth where the energy of these visible bands, received at the satellite sensor, is inversely proportional to the depth of water. However, without satisfying homogeneity of the seafloor topography, this linear method fails. The current state-of-the-art is represented by artificial neural network (ANN) models, which were developed using a non-linear, static modeling function. However, more accurate modeling can be achieved using a highly non-linear, dynamic modeling function. This paper investigates a highly non-linear wavelet network model for accurate satellite-based bathymetric modeling with dynamic non-linear wavelet activation function that has been proven to be a valuable modeling method for many applications. Freely available Level-1C satellite imagery from the Sentinel-2A satellite was employed to develop and justify the proposed wavelet network model. The top-of-atmosphere spectral reflectance values for the multispectral bands were employed to establish the wavelet network model. It is shown that the root-mean-squared (RMS) error of the developed wavelet network model was about 1.82 m, and the correlation between the wavelet network model depth estimate and “truth” nautical chart depths was about 95%, on average. To further justify the proposed model, a comparison was made among the developed, highly non-linear wavelet network method, the Stumpf log-ratio method, and the ANN method. It is concluded that the developed, highly non-linear wavelet network model is superior to the Stumpf log-ratio method by about 37% and outperforms the ANN model by about 21%, on average, on the basis of the RMS errors. Also, the accuracy of the bathymetry-derived wavelet network model was evaluated on the basis of the International Hydrographic Organization (IHO)’s standards for all survey orders. It is shown that the accuracy of the bathymetry derived from the wavelet network model does not meet the IHO’s standards for all survey orders; however, the wavelet network model can still be employed as an accurate and powerful tool for survey planning when conducting hydrographic surveys for new, shallow water areas.

Description: Road rutting caused by vehicle loading in the wheel path is a major form of asphalt pavement distress. Hydroplaning and loss of skid resistance are directly related to high road rutting severity. Periodical measurements of rut depth are crucial to maintenance and rehabilitation planning. In this study, we explored the feasibility of using point clouds gathered by Mobile LiDAR systems to measure the rut depth. These point clouds that are collected along roads are usually used for other purposes, namely asset inventory or topographic survey. Taking advantage of available clouds to identify rutting severity in critical pavement areas can result in considerable economic and time saving and thus, added value, when compared with specific expensive rut measuring systems. Four different strategies of cloud points aggregation are presented to create the cross-section of points. Such strategies were established to improve the precision of individual sensor measurements. Despite the 5 mm precision of the used system, it was possible to estimate rut depth values that were slightly inferior. The rut depth values obtained from each cross-section strategy were compared with the manual field measured values. The cross-sections based on averaged cloud points sensor profile aggregation was revealed to be the most suitable strategy to measure rut depth. Despite the fact that the study was specifically conducted to measure rut depth, the evaluation results show that the methodology can also be useful for other mobile LiDAR point clouds cross-sections applications.

Description: All authors of the published article [...]

Description: Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests’ results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster’s member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems.

Description: The timely and proper rehabilitation of damaged roads is essential for road maintenance, and an effective method to detect road surface distress with high efficiency and low cost is urgently needed. Meanwhile, unmanned aerial vehicles (UAVs), with the advantages of high flexibility, low cost, and easy maneuverability, are a new fascinating choice for road condition monitoring. In this paper, road images from UAV oblique photogrammetry are used to reconstruct road three-dimensional (3D) models, from which road pavement distress is automatically detected and the corresponding dimensions are extracted using the developed algorithm. Compared with a field survey, the detection result presents a high precision with an error of around 1 cm in the height dimension for most cases, demonstrating the potential of the proposed method for future engineering practice.

Description: Urban roads are the lifeline of urban transportation and satisfy the commuting and travel needs of citizens. Following the acceleration of urbanization and the frequent extreme weather in recent years, urban waterlogging is occurring more than usual in summer and has negative effects on the urban traffic networks. Extracting flooded roads is a critical procedure for improving the resistance ability of roads after urban waterlogging occurs. This paper proposes a flooded road extraction method to extract the flooding degree and the time at which roads become flooded in large urban areas by using global positioning system (GPS) trajectory points with driving status information and the high position accuracy of vector road data with semantic information. This method uses partition statistics to create density grids (grid layer) and uses map matching to construct a time-series of GPS trajectory point density for each road (vector layer). Finally, the fusion of grids and vector layers obtains a more accurate result. The experiment uses a dataset of GPS trajectory points and vector road data in the Wuchang district, which proves that the extraction result has a high similarity with respect to the flooded roads reported in the news. Additionally, extracted flooded roads that were not reported in the news were also found. Compared with the traditional methods for extracting flooded roads and areas, such as rainfall simulation and SAR image-based classification in urban areas, the proposed method discovers hidden flooding information from geospatial big data, uploaded at no cost by urban taxis and remaining stable for a long period of time.

Description: The traditional ways of measuring global sustainable development and economic development schemes and their progress suffer from a number of serious shortcomings. Remote sensing and specifically nighttime light has become a popular supplement to official statistics by providing an objective measure of human settlement that can be used as a proxy for population and economic development measures. With the increased availability and use of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and data in social science, it has played an important role in data collection, including measuring human development and economic growth. Numerous studies are using nighttime light data to analyze dynamic regions such as expansions of urban areas and rapid industrialization often highlight the problem of saturation in urban centers with high light intensity. However, the quality of nighttime light data and its appropriateness for analyzing areas and regions with low and fluctuating levels of light have rarely been questioned or studied. This study examines the accuracy of DMSP-OLS and VIIRS-DNB by analyzing 147 communities in Burkina Faso to provide insights about problems related to the study of areas with a low intensity of nighttime light during the studied period from 1992 to 2012. It found that up to 57% of the communities studied were undetectable throughout the period, and only 9% of communities studied had a 100% detection rate. Unsurprisingly, the result provides evidence that detection rates in both datasets are particularly low (3%) for settlements with 0–9999 inhabitants, as well as for larger settlements with population of 10,000–24,999 (28%). Cross-checking with VIIRS-DNB for the year 2012 shows similar results. These findings suggest that careful consideration must be given to the use of nighttime light data in research and global comparisons to monitor the progress of the United Nation’s Sustainable Development Goals, especially when including developing countries with areas containing low electrification rates and low population density.

Description: China’s rapid urbanization and industrialization have continually placed massive pressure on the country’s natural resources. The fragmented departmental administration of natural resources also intensifies the problem of sustainable use. Accordingly, China’s central government has launched natural resource administration reform from decentralization to unification. This study systematically analyzes the reform requirements from legal, organizational, and technical aspects. The right structure of China’s natural resource assets for fulfilling such requirements is examined in this work through a review of relevant legal text, and such a right structure is converted into a draft national technical standard of China’s natural resource administration on the basis of the land administration domain model (LADM). Results show that China’s natural resource administration covers lands, buildings, structures, forests, grasslands, waters, beaches, sea areas, minerals, and other fields. The types of private rights over natural resources include ownerships, land-contracted management rights (cultivated land, forest land, grassland, and water area), rights to use construction land (state-owned and collective-owned), rights to use agricultural land, rights to use homestead land, breeding rights on water areas and beaches, rights to use sea areas, rights to use uninhabited islands, and mining rights. The types of public rights over natural resources include comprehensive land use, urban and rural, sea use, and territory space planning. Furthermore, various types of these property rights can be converted into corresponding classes in LADM on the basis of the analysis of the property subject, object, and rights.

Description: Public transport plays an important role in developing sustainable cities. A better understanding of how different public transit modes (bus, metro, and taxi) interact with each other will provide better sustainable strategies to transport and urban planners. However, most existing studies are either limited to small-scale surveys or focused on the identification of general interaction patterns during times of regular traffic. Transient demographic changes in a city (i.e., many people moving out and in) can lead to significant changes in such interaction patterns and provide a useful context for better investigating the changes in these patterns. Despite that, little has been done to explore how such interaction patterns change and how they are linked to the built environment from the perspective of transient demographic changes using urban big data. In this paper, the tap-in-tap-out smart card data of bus/metro and taxi GPS trajectory data before and after the Chinese Spring Festival in Shenzhen, China, are used to explore such interaction patterns. A time-series clustering method and an elasticity change index (ECI) are adopted to detect the changing transit mode patterns and the underlying dynamics. The findings indicate that the interactions between different transit modes vary over space and time and are competitive or complementary in different parts of the city. Both ordinary least-squares (OLS) and geographically weighted regression (GWR) models with built environment variables are used to reveal the impact of changes in different transit modes on ECIs and their linkage with the built environment. The results of this study will contribute to the planning and design of multi-modal transport services.

Description: Three-dimensional (3D) pipe network modeling plays an essential part in high performance-based smart city applications. Given that massive 3D pipe networks tend to be difficult to manage and to visualize, we propose in this study a hybrid framework for high-performance modeling of a 3D pipe network, including pipe network data model and high-performance modeling. The pipe network data model is devoted to three-dimensional pipe network construction based on network topology and building information models (BIMs). According to the topological relationships of the pipe point pipelines, the pipe network is decomposed into multiple pipe segment units. The high-performance modeling of 3D pipe network contains a spatial 3D model, the instantiation, adaptive rendering, and combination parallel computing. Spatial 3D model (S3M) is proposed for spatial data transmission, exchange, and visualization of massive and multi-source 3D spatial data. The combination parallel computing framework with GPU and OpenMP was developed to reduce the processing time for pipe networks. The results of the experiments showed that the hybrid framework achieves a high efficiency and the hardware resource occupation is reduced.

Description: Quickly and efficiently monitoring soil nutrient contents using remote sensing technology is of great significance for farmland soil productivity, food security and sustainable agricultural development. Current research has been conducted to estimate and map soil nutrient contents in large areas using hyper-spectral techniques, however, it is difficult to obtain accurate estimates. In order to improve the estimation accuracy of soil nutrient contents, we introduced a GA-BPNN method, which combined a back propagation neural network (BPNN) with the genetic algorithm optimization (GA). This study was conducted in Guangdong, China, based on soil nutrient contents and hyperspectral data. The prediction accuracies from a partial least squares regression (PLSR), BPNN and GA-BPNN were compared using field observations. The results showed that (1) Among three methods, the GA-BPNN provided the most accurate estimates of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) contents; (2) Compared with the BPNN models, the GA-BPNN models significantly improved the estimation accuracies of the soil nutrient contents by decreasing the relative root mean square error (RRMSE) values by 15.9%, 5.6% and 20.2% at the sample point level, and 20.1%, 16.5% and 47.1% at the regional scale for TN, TP and TK, respectively. This indicated that by optimizing the parameters of BPNN, the GA-BPNN provided greater potential to improving the estimation; and (3) Soil TK content could be more accurately mapped by the GA-BPNN method using HuanJing-1A Hyperspectral Imager (HJ-1A HSI) (manufacturer: China Aerospace Science and Technology Corporation; Beijing, China) data with a RRMSE value of 20.37% than the soil TN and TP with the RRMSE values of 40.41% and 34.71%, respectively. This implied that the GA-BPNN model provided the potential to map the soil TK content for the large area. The research results provided an important reference for high-accuracy prediction of soil nutrient contents.

Description: The fractional vegetation cover (FVC) data measured on the ground is the main source for the calibration and verification of FVC remote sensing inversion, and its accuracy directly affects the accuracy of remote sensing inversion results. However, the existing research on the evaluation of the accuracy of the field quadrat survey of FVC based on the satellite remote sensing pixel scale is inadequate, especially in the alpine grassland of the Qinghai-Tibet Plateau. In this paper, five different alpine grasslands were examined, the accuracy of the FVC obtained by the photography method was analyzed, and the influence of the number of samples on the field survey results was studied. First, the results show that the threshold method could accurately extract the vegetation information in the photos and obtain the FVC with high accuracy and little subjective interference. Second, the number of samples measured on the ground was logarithmically related to the accuracy of the FVC of the sample plot (p 〈 0.001). When the number of samples was larger, the accuracy of the FVC of the sample plot was higher and closer to the real value, and the stability of data also increased with the increase of the number of samples. Third, the average FVC of the measured quadrats on the ground was able to represent the FVC of the sample plot, but on the basis that there were enough measured quadrats. Finally, the results revealed that the degree of fragmentation reflecting the state of ground vegetation affects the acquisition accuracy of FVC. When the degree of fragmentation of the sample plot is higher, the number of samples needed to achieve the accuracy index is higher. Our results suggest that when obtaining the FVC on the satellite remote sensing pixel scale, the number of samples measured on the ground is an important factor affecting the accuracy, which cannot be ignored.

Description: Population is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential treatment of the spatial stationarity and non-stationarity of variables. Based on a semi-parametric, geographically weighted regression model (s-GWR), this paper attempts to construct a novel, precise population spatialization method considering parametric stationarity to enhance spatialization accuracy; the southwestern area of China is used as the study area for comparison and validation. In this study, the night-time light and land use data were integrated as weighting factors to establish the population model; based on the analysis of variables characteristics, the method uses an s-GWR model to deal with the spatial stationarity of variables and reduce regional errors. Finally, the spatial distribution of the population (SSDP) of the study area in 2010 was obtained. When assessed against the traditional regression models, the model that considers parametric stationarity is more accurate than the models without it. Furthermore, the comparison with three commonly-used population grids reveals that the SSDP has a percentage error close to zero at the county level, while at the township level, the mean relative error of SSDP is 33.63%, and that is 〉15% better than other population grids. Thus, this study suggests that the proposed method can produce a more accurate population distribution.

Description: High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public safety. Concretely, SLCFF predicts the number of people that will arrive at or depart from stations in a given period. However, one challenge is that the crowd flows across hundreds of stations irregularly scattered throughout a city are affected by complicated spatio-temporal events. Additionally, some external factors such as weather conditions or holidays may change the crowd flow tremendously. In this paper, a spatio-temporal U-shape network model (ST-Unet) for SLCFF is proposed. It is a neural network-based multi-output regression model, handling hundreds of target variables, i.e., all stations’ in and out flows. ST-Unet emphasizes stations’ spatial dependence by integrating the crowd flow information from neighboring stations and the cluster it belongs to after hierarchical clustering. It learns the temporal dependence by modeling the temporal closeness, period, and trend of crowd flows. With proper modifications on the network structure, ST-Unet is easily trained and has reliable convergency. Experiments on four real-world datasets were carried out to verify the proposed method’s performance and the results show that ST-Unet outperforms seven baselines in terms of SLCFF.

Description: Evacuation plans are critical in case of natural disaster to save people’s lives. The priority of population evacuation on coastal areas could be useful to reduce the death toll in case of tsunami hazard. In this study, the population density remote sensing mapping approach was developed using population records in 2013 and Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) night-time light (NTL) image of the same year for defining the coastal densest resident areas in Pearl River Estuary (PRE), China. Two pixel-based saturation correction methods were evaluated for application of population density mapping to enhance DMSP/OLS NTL image. The Vegetation Adjusted NTL Urban Index (VANUI) correction method (R2 (original/corrected): 0.504, Std. error: 0.0069) was found to be the better-fit correction method of NTL image saturation for the study area compared to Human Settlement Index (HSI) correction method (R2 (original/corrected): 0.219, Std. error: 0.1676). The study also gained a better dynamic range of HSI correction (0~25 vs. 0.1~5.07) compared to the previous one [27]. The town-level’s population NTL simulation model is built (R2 = 0.43, N = 47) for the first time in PRE with mean relative error (MSE) of 32% (N = 24, town level), On the other side, the tsunami hazard map was produced based on numerical modeling of potential tsunami wave height and velocity, combining with the river net system, elevation, slope, and vegetation cover factors. Both results were combined to produce an evacuation map in PRE. The simulation of tsunami exposure on density of population showed that the highest evacuation priority was found to be in most of Zhuhai city area and the coastal area of Shenzhen City under wave height of nine meters, while lowest evacuation priority was defined in Panyu and Nansha Districts of Guangzhou City, eastern and western parts of Zhongshan City, and northeast and northwest parts of Dongguan City. The method of tsunami risk simulation and the result of mapped tsunami exposure are of significance for direction to tsunami disaster-risk reduction or evacuation traffic arrangement in PRE or other coastal areas in the world.

Description: Assessing the access to fire service at an urban scale involves accounting for geographical impedance, demand, and supply, thus both spatial and non-spatial dimensions must be taken into account. Therefore, in this paper, an optimized two-step floating catchment area (F-2SFCA) method is proposed for measuring urban fire service access, which incorporates the effects of both spatial and non-spatial factors into fire service access. The proposed model is conducted in a case study to assess the fire service accessibility of Nanjing City, China, and then compares its differences and strengths to the existing 2SFCA (two-step floating catchment area) methods. The experimental results demonstrate that the proposed method effectively quantifies the actual fire service needs and reflects a more realistic spatial pattern of accessibility (i.e., high accessibility level corresponded to a low fire service needs). In addition, we teste the relationship between service accessibility and the facility busyness using the inverted 2SFCA method. The empirical findings indicate that the weighted average accessibility obtained by F-2SFCA is reciprocal to facility busyness across the study area (based on a 5-min catchment), and fits an obvious nonlinear correlation with the high R-square values. The above results further prove the effectiveness and accuracy of the proposed method in characterizing the accessibility of fire services.

Description: The effectiveness of disaster response depends on the correctness and timeliness of data regarding the location and the impact of the event. These two issues are critical when the data come from citizens’ tweets, since the automatic classification of disaster-related tweets suffers from many shortcomings. In this paper, we explore an approach based on participatory sensing (i.e., a subset of mobile crowdsourcing that emphasizes the active and intentional participation of citizens to collect data from the place where they live or work). We operate with the hypothesis of a “friendly world”, that is by assuming that after a calamitous event, in the survivors prevails the feeling of helping those who suffer. The extraction, from the Twitter repository, of the few tweets relevant to the event of interest has a long processing time. With the aggravating circumstance in the phase that follows a severe earthquake, the elaboration of tweets clashes with the need to act promptly. Our proposal allows a huge reduction of the processing time. This goal is reached by introducing a service and a mobile app, the latter is an intermediate tool between Twitter and the citizens, suitable to assist them to write structured messages that act as surrogates of tweets. The article describes the architecture of the software service and the steps involved in the retrieval, from the Twitter server, of the messages coming from citizens living in the places hit by the earthquake; moreover, it details the storage of those messages into a geographical database and their processing using SQL.

Description: 3D urban building models, which provide 3D information services for urban planning, management and operational decision-making, are essential for constructing digital cities. Unfortunately, the existing reconstruction approaches for LoD3 building models are insufficient in model details and are associated with a heavy workload, and accordingly they could not satisfy urgent requirements of realistic applications. In this paper, we propose an accurate LoD3 building reconstruction method by integrating multi-source laser point clouds and oblique remote sensing imagery. By combing high-precision plane features extracted from point clouds and accurate boundary constraint features from oblique images, the building mainframe model, which provides an accurate reference for further editing, is quickly and automatically constructed. Experimental results show that the proposed reconstruction method outperforms existing manual and automatic reconstruction methods using both point clouds and oblique images in terms of reconstruction efficiency and spatial accuracy.

Description: The rapid growth of positioning technology allows tracking motion between places, making trajectory recordings an important source of information about place connectivity, as they map the routes that people commonly perform. In this paper, we utilize users’ motion traces to construct a behavioral representation of places based on how people move between them, ignoring geographical coordinates and spatial proximity. Inspired by natural language processing techniques, we generate and explore vector representations of locations, traces and visitors, obtained through an unsupervised machine learning approach, which we generically named motion-to-vector (Mot2vec), trained on large-scale mobility data. The algorithm consists of two steps, the trajectory pre-processing and the Word2vec-based model building. First, mobility traces are converted into sequences of locations that unfold in fixed time steps; then, a Skip-gram Word2vec model is used to construct the location embeddings. Trace and visitor embeddings are finally created combining the location vectors belonging to each trace or visitor. Mot2vec provides a meaningful representation of locations, based on the motion behavior of users, defining a direct way of comparing locations’ connectivity and providing analogous similarity distributions for places of the same type. In addition, it defines a metric of similarity for traces and visitors beyond their spatial proximity and identifies common motion behaviors between different categories of people.

Description: This paper presents an object-based approach to mapping a set of landforms located in the fluvio-eolian plain of Rio Dulce and alluvial plain of Rio Salado (Dry Chaco, Argentina), with two Landsat 8 images collected in summer and winter combined with topographic data. The research was conducted in two stages. The first stage focused on basic-spectral landform classifications where both pixel- and object-based image analyses were tested with five classification algorithms: Mahalanobis Distance (MD), Spectral Angle Mapper (SAM), Maximum Likelihood (ML), Support Vector Machine (SVM) and Decision Tree (DT). The results obtained indicate that object-based analyses clearly outperform pixel-based classifications, with an increase in accuracy of up to 35%. The second stage focused on advanced object-based derived variables with topographic ancillary data classifications. The combinations of variables were tested in order to obtain the most accurate map of landforms based on the most successful classifiers identified in the previous stage (ML, SVM and DT). The results indicate that DT is the most accurate classifier, exhibiting the highest overall accuracies with values greater than 72% in both the winter and summer images. Future work could combine both, the most appropriate methodologies and combinations of variables obtained in this study, with physico-chemical variables sampled to improve the classification of landforms and even of types of soil.

Description: In recent years, various types of terrorist attacks have occurred which have caused worldwide catastrophes. The ability to proactively detect and even predict a potential terrorist risk is critically important for government agencies to react in a timely manner. In this study, a method of geospatial statistics was used to analyse the spatio-temporal evolution of terrorist attacks on the Indochina Peninsula. The machine learning random forest (RF) method was adopted to predict the potential risk of terrorist attacks on the Indochina Peninsula on a spatial scale with 15 driving factors. The RF model performed well with AUC values of 0.839 [95% confidence interval of 0.833–0.844]. The map of the potential distribution of terrorist attack risk was obtained with a 0.05×0.05-degree (approximately 5×5 km) resolution. The results indicate that Thailand is the most dangerous area for terrorist attacks, especially southern Thailand, Bangkok and its surrounding cities. Middle Cambodia and the northern and southern parts of Myanmar are also high-risk areas. Other areas are relatively low risk. This study provides the hotspots for terrorist attacks on a more fine-grained geographical unit. Meanwhile, it shows that machine learning algorithms (e.g., RF) combined with GIS have great potential for simulating the risk of terrorist attacks.

Description: Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats and the ecosystem in a negative manner. Hence, urbanization-related changes should be monitored regularly, and land cover maps should be updated to reflect the current situation. This research presents a comparative evaluation of two classification algorithms, pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, in producing a dynamic land cover map of the Istanbul metropolitan city in Turkey between 2013 and 2017 using Landsat 8 Operational Land Imager (OLI) multi-temporal satellite images. Additionally, the efficiencies of the two data dimension reduction methods are evaluated as part of this research. For dimension reduction, built-up index (BUI) and principal component analysis (PCA) data were calculated for five images during the mentioned period, and the classification algorithms were applied on data stacks for each dimension reduction method. The classification results indicate that the GEOBIA classification of the BUI data set provided the highest accuracy, with a 91.60% overall accuracy and 0.91 kappa value. This combination was followed by the GEOBIA classification of the PCA data set, which highlights the overall efficiency of the GEOBIA over the SVM method. On the other hand, the BUI data set provided more reliable and consistent results for urban expansion classes due to representing physical responses of the surface when compared to the data set of the PCA, which is a spectral transformation method.

Description: In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

Description: Viewshed analysis is of great interest to location optimization, environmental planning, ecology and tourism. There have been plenty of viewshed analysis methods which are generally time-consuming and among these methods, the XDraw algorithm is one of the fastest algorithms and has been widely adopted in various applications. Unfortunately, XDraw suffers from chunk distortion which greatly lowers the accuracy, which limits the application of XDraw to a certain extent. Previous works failed to remove chunk distortion because they are unaware of the underlying contribution relationship. In this paper, we propose HiXDraw—an improved XDraw algorithm free of chunk distortion. We first uncover the causation of chunk distortion from an innovative contributing perspective. Instead of recording LOS (line-of-sight) height, we use a new auxiliary grid to preserve contributing points. By preventing improper terrain data from contributing to determining the visibility, we significantly improve the accuracy of the outcome viewshed. The experimental results reveal that the error rate largely decreases by 65%. Given the same computing time, HiXDraw is more accurate than previous improvements in XDraw. To validate the removal of chunk distortion, we also present a pillar experiment.

Description: Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively.

Description: The mountainous region of Greater Sochi, including the Olympic ski-jump complex area, located in the northern Caucasus, is always subjected to landslides. The weathered mudstone of low strength and potential high-intensity earthquakes are considered as the crucial factors causing slope instability in the ski-jump complex area. This study aims to conduct a seismic slope instability map of the area. A slope map was derived from a digital elevation model (DEM) and calculated using ArcGIS. The numerical modelling of slope stability with various slope angles was conducted using Geostudio. The Spencer method was applied to calculate the slope safety factors (Fs). The pseudostatic analysis was used to compute Fs considering seismic effect. A good correlation between Fs and slope angle was found. Combining these data, sets slope instability maps were achieved. Newmark displacement maps were also drawn according to empirical regression equations. The result shows that the static safety factor map corresponds to the existing slope instability locations in a shallow landslide inventory map. The seismic safety factor maps and Newmark displacement maps may be applied to predict potential landslides of the study area in the case of earthquake occurrence.

Description: The rapid increase in applications of Light Detection and Ranging (LiDAR) scanners, followed by the development of various methods that are dedicated for survey data processing, visualization, and dissemination constituted the need of new open standards for storage and online distribution of collected three-dimensional data. However, over a decade of research in the area has resulted in a number of incompatible solutions that offer their own ways of disseminating results of LiDAR surveys (be it point clouds or reconstructed three-dimensional (3D) models) over the web. The article presents a unified system for remote processing, storage, visualization, and dissemination of 3D LiDAR survey data, including 3D model reconstruction. It is built with the use of open source technologies and employs open standards, such as 3D Tiles, LASer (LAS), and Object (OBJ) for data distribution. The system has been deployed for automatic organization, processing, and dissemination of LiDAR surveys that were performed in the city of Gdansk. The performance of the system has been measured using a selection of LiDAR datasets of various sizes. The system has shown to considerably simplify the process of data organization and integration, while also delivering tools for easy discovery, inspection, and acquisition of desired datasets.

Description: Watersheds represent natural units of social–ecological systems and affect crop productivity. Extreme weather events accelerate the natural erosion process by triggering more landslides in watersheds. To achieve the land degradation neutrality set up by the UN’s Sustainable Development Goals, it is necessary to assess and map spatiotemporal landslides in watersheds. This paper proposes an innovative approach to calculating the instability index by preparing an annual landslide inventory, determining the optimum sub-watershed, compensating for shadow effects on the time series of the landslide area ratio, and classifying the standard deviations to different levels of instability. Taking the Qingquan watershed as an example, the instability index calculated for 22 sub-watersheds makes it possible to identify hot spots that are prone to collapse. This new index can also be used to evaluate the effectiveness of watershed management before and after completion of a specific engineering project, as well as to update the latest upriver situation to evaluate current management practices and develop strategies for future planning. Based on this new approach, the Soil and Water Conservation Bureau of Taiwan assesses the stability of 28 watersheds, and the results are made available on the Big Geospatial Information System.

Description: The prevalent use of GPS-based navigation systems impairs peoples’ ability to orient themselves. This paper investigates whether wayfinding maps that accentuate different types of environmental features support peoples’ spatial learning. A virtual-reality driving simulator was used to investigate spatial knowledge acquisition in assisted wayfinding tasks. Two main conditions of wayfinding maps were tested against a base condition: (i) highlighting local features, i.e., landmarks, along the route and at decision points; and (ii) highlighting structural features that provide global orientation. The results show that accentuating local features supports peoples’ acquisition of route knowledge, whereas accentuating global features supports peoples’ acquisition of survey knowledge. The results contribute to the general understanding of spatial knowledge acquisition in assisted wayfinding tasks. Future navigation systems could enhance spatial knowledge by providing visual navigation support incorporating not only landmarks but structural features in wayfinding maps.

Description: Based on the analysis of the problems in the generation algorithm of discrete grid systems domestically and abroad, a new universal algorithm for the unit duplication of a polyhedral discrete grid is proposed, and its core is “simple unit replication + effective region restriction”. First, the grid coordinate system and the corresponding spatial rectangular coordinate system are established to determine the rectangular coordinates of any grid cell node. Then, the type of the subdivision grid system to be calculated is determined to identify the three key factors affecting the grid types, which are the position of the starting point, the length of the starting edge, and the direction of the starting edge. On this basis, the effective boundary of a multiscale grid can be determined and the grid coordinates of a multiscale grid can be obtained. A one-to-one correspondence between the multiscale grids and subdivision types can be established. Through the appropriate rotation, translation and scaling of the multiscale grid, the node coordinates of a single triangular grid system are calculated, and the relationships between the nodes of different levels are established. Finally, this paper takes a hexagonal grid as an example to carry out the experiment verifications by converting a single triangular grid system (plane) directly to a single triangular grid with a positive icosahedral surface to generate a positive icosahedral surface grid. The experimental results show that the algorithm has good universality and can generate the multiscale grid of an arbitrary grid configuration by adjusting the corresponding starting transformation parameters.

Description: In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, along with historical lightning-caused fire occurrences during the 2005–2016 period, derived from a Moderate Resolution Imaging Spectroradiometer. First, we calculated the normalized difference water index (NDWI) as an indicator of vegetation/fuel water content over the six natural subregions of interest. Then, we generated the subregion-specific annual dynamic median NDWI during the 2005–2012 period, which was assembled into a distinct pattern every year. We plotted the historical lightning-caused fires onto the generated patterns, and used the concept of cumulative frequency to model lightning-caused fire occurrences. Then, we applied this concept to model the cumulative frequencies of lightning-caused fires using the median NDWI values in each natural subregion. By finding the best subregion-specific function (i.e., R2 values over 0.98 for each subregion), we evaluated their performance using an independent subregion-specific lightning-caused fire dataset acquired during the 2013–2016 period. Our analyses revealed strong relationships (i.e., R2 values in the range of 0.92 to 0.98) between the observed and modeled cumulative frequencies of lightning-caused fires at the natural subregion level throughout the validation years. Finally, our results demonstrate the applicability of the proposed method in modeling lightning-caused fire occurrences over forested regions.

Description: Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affecting the predictive accuracy via a prediction accuracy analysis and prediction location evaluation. The findings of this paper can provide intelligence for the improvement of taxi services, to increase the passenger capacity of taxis and also to improve the probability of passengers finding taxis.

Description: AU-Agenda 2063 was adopted at the 24th Ordinary Session of the African Heads of State and Government in 2015 as the blueprint for the future development of the continent. Built upon the continent’s past experiences, challenges, and successes, AU-Agenda 2063 comprehensively describes the strategic path for Africa’s future development in the next 50 years. Thus, the monitoring of its implementation in various African states is critical for ensuring sustainable development and track progress. However, the higher cost of collecting data for accurately and reliably monitoring the implementation of Agenda 2063 may hinder the progress towards achieving these goals. Satellite Earth observation provides ample data, and thus has provided opportunities for the development of novel products and services with the potential to support implementation, monitoring and reporting for AU-Agenda 2063 development imperatives. However, it has been limitedly exploited in Africa, as evidenced by lower research outputs and investments. This calls for increased capacity building in the use of available EO data and products for various users including decision makers to advance national, regional and continental priorities. The use of such data products is often hampered by the capability to understand the products and thus their value for addressing socio-economic challenges. This paper discusses the potential of Earth observation capacity building for supporting the implementation, monitoring of, and reporting towards achieving AU-Agenda 2063 development imperatives. Specifically, this paper identifies existing capacity building resources, including the role of open and free Earth observation data, open-source software, and product dissemination platforms that can be leveraged for supporting national development, service delivery and the achievement of AU-Agenda 2063 targets. Furthermore, the paper recognizes the importance of bilateral and multilateral partnerships in leveraging existing know-how, technology and other resources for advancing strategic goals of African emerging space agencies and promoting sustainable development, with examples from South African National Space Agency (SANSA). Then, the challenges and opportunities for capacity building and the wide adoption of EO in Africa are discussed in the context of AU-Agenda 2063. The paper thus concludes that EO capacity building is essential to address the skills and data gaps and increase the use of EO-based solutions for decision making in various sectors, critical for achieving AU-A2063.

Description: The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user’s requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals.

Description: It is meaningful to analyze urban spatial structure by identifying urban subcenters, and many methods of doing so have been proposed in the published literature. Although these methods are widely applied, they exhibit obvious shortcomings that limit their further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we propose the density contour tree (DCT) method for detecting urban polycentric structures and their spatial distributions. Conceptually, this method is based on an analogy between urban spatial structure and terrain. The point-of-interest (POI) density is visualized as a continuous mathematical surface representing the urban terrain. Peaks represent the regions of the most frequent human activity, valleys represent regions with small population densities in the city, and slopes represent spatial changes in urban land-use intensity. Using this method, we have detected the urban “polycentric” structure of Beijing and determined the corresponding spatial relationships. In addition, several important properties of the urban centers have been identified. For example, Beijing has a typical urban polycentric structure with an urban center area accounting for 5.9% of the total urban area, and most of the urban centers in Beijing serve comprehensive functions. In general, the method and the results can serve as references for the later research on analyzing urban structure.

Description: With the rapid development of the economy, urgent needs for 3-D Geographical Information System (GIS) have sprung up in many application fields. The precise expression of three-dimensional topological relations is the foundation of spatial analysis, topological query, and spatial reasoning in three-dimensional space. In this paper, we subdivide the topological part “boundary” into face, edge, and vertex and propose a 25-intersection model (25IM) to represent topological relations between two simple spatial objects (point, line, region, and body) in 3-D space. An object in the 25IM has five topological parts: vertex, edge, face, interior, and exterior. The classification of topological relations is simplified by merging contain/inside and cover/coveredby. The 25IM describes ten groups of topological relations: body/body, body/region, body/line, body/point, region/region, region/line, region/point, line/line, line/point, and point/point. The 25IM is demonstrated to be more expressive than the 9IM and the DE-9IM, especially in distinguishing the detail situations when one object meets/covers another object (e.g., two bodies meet/cover at vertex, edge, or face).

Description: Digital elevation model (DEM) resolution is closely related to the degree of expression of real terrain, the extraction of terrain parameters, and the uncertainty of statistical models. Therefore, based on DEMs with various resolutions, this paper explores the representation and distinguishing ability of different roughness algorithms to measure terrain parameters. Fuyang, a district of Hangzhou City with various landform types, was selected as the research area. Slope, root mean squared height, vector deviation, and two-dimensional continuous wavelet transform were selected as four typical roughness algorithms. The resolutions used were 5, 10, 25, and 50 m DEM on the scale for plains, hills, and mountainous areas. The statistical criteria of effect size and entropy were used as indicators to evaluate and analyze the different roughness algorithms. The results show that in terms of these measures: (1) The expression ability of the SLOPE and root mean squared height (RMSH) algorithms is better than that of the vector deviation method, while the two-dimensional continuous wavelet method based on frequency analysis emphasizes the terrain information within a certain range. (2) The terrain distinguishing ability of the SLOPE and RMSH is not sensitive to the changes in resolution, with the other two algorithms varying with the changes in resolution.

Description: Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.

Description: Marine oil spills seriously impact the marine environment and transportation. When oil spill accidents occur, oil spill distribution information, in particular, the relative thickness of the oil film, is vital for emergency decision-making and cleaning. Hyperspectral remote sensing technology is an effective means to extract oil spill information. In this study, the concept of deep learning is introduced to the classification of oil film thickness based on hyperspectral remote sensing technology. According to the spatial and spectral characteristics, the stacked autoencoder network model based on the support vector machine is improved, enhancing the algorithm’s classification accuracy in validating data sets. A method for classifying oil film thickness using the convolutional neural network is designed and implemented to solve the problem of space homogeneity and heterogeneity. Through numerous experiments and analyses, the potential of the two proposed deep learning methods for accurately classifying hyperspectral oil spill data is verified.

Description: This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails.

Description: Probabilistic time geography uses a fixed distance threshold for the definition of the encounter events of moving objects. However, because of the distance-decay effect, different distances within the fixed threshold ensure that the encounter events do not always have the same possibility, and, therefore, the quantitative probabilistic time geography analysis needs to consider the actual distance-decay coefficient (DDC). Thus, this paper introduces the DDC and proposes a new encounter probability measure model that takes into account the distance-decay effect. Given two positions of a pair of moving objects, the traditional encounter probability model is that if the distance between the two positions does not exceed a given threshold, the encounter event may occur, and its probability is equal to the product of the probabilities of the two moving objects in their respective positions. Furthermore, the probability of the encounter at two given positions is multiplied by the DDC in the proposed model, in order to express the influence of the distance-decay effect on the encounter probability. Finally, the validity of the proposed model is verified by an experiment, which uses the tracking data of wild zebras to calculate the encounter probability, and compares it with the former method.

Description: The importance of the distribution of accommodation businesses over a certain area has grown remarkably, especially if such distribution is mapped using tools and techniques that utilize the territory as a variable in the analysis. The purpose of this paper is to demonstrate, by means of a geographical information system (GIS) and spatial statistics, that it is possible to better define the groupings of rural accommodation available in Extremadura, Spain, especially if these are conceptualized by dint of their lodging capacity. To do so, two specific techniques have been used: hotspot analysis and outlier analysis, which yield results that prove the existence of homogeneous and heterogeneous groups of accommodation businesses, based not only on their spatial proximity but also on their lodging capacity. On the basis of this analysis, the regional administration can devise tourist policies and strategic plans in order to improve the management and efficiency of each business. Despite the applicability of the present results, this study also addresses the difficulties in using these techniques—Where establishing the spatial relationships and the boundary distance are key concepts. In the case study here, the ideal configuration utilizes a fixed distance of six miles.

Description: The design of a natural gas pipeline route is a very important stage in Natural Gas Transmission Pipeline projects. It is a very complicated process requiring many different criteria for various areas to be evaluated simultaneously. These criteria include geographical, social, economic, and environmental aspects with their obstacles. In the classical approach, the optimum route design is usually determined manually with gathering the spatial references for suitable places and obstructions from the ground. This traditional method is not effective because it does not consider all the factors that affect the route of the pipeline. Today, the powerful tools incorporated in Geographical Information Systems (GIS) can be used to automatically determine the optimum route. An automatic pipeline route finder algorithm can calculate the best convenient route avoiding geographic and topological obstructs and selecting suitable places depending on their weights. In this study, an automatic natural gas pipeline design study was carried out in the east western region of Turkey. At the end of the study, an automatic natural gas pipeline route was determined using GIS and a least cost path algorithm, and an alternative study was conducted using a traditional method. In addition, a cartographic line simplification process with a point removal algorithm was used to eliminate the high vertex points and a simplified route was determined. The results were compared with the results of a finished Muş natural gas project constructed by The Turkish Petroleum Pipeline Corporation (BOTAŞ) and the negative and positive effects were evaluated. It was concluded that the use of GIS capabilities and the lowest cost path distance algorithm resulted in a 20% reduction of the cost through the simplification.

Description: In this study, different in-situ and close-range sensing surveying techniques were compared based on the spatial differences of the resultant datasets. In this context, the DJI Phantom 3 Advanced and Trimble UX5 Unmanned Aerial Vehicle (UAV) platforms, Zoller + Fröhlich 5010C phase comparison for continuous wave-based Terrestrial Laser Scanning (TLS) system and Network Real Time Kinematic (NRTK) Global Navigation Satellite System (GNSS) receiver were used to obtain the horizontal and vertical information about the study area. All data were collected in a gently (mean slope angle 4%) inclined, flat vegetation-free, bare-earth valley bottom near Istanbul, Turkey (the size is approximately 0.7 ha). UAV data acquisitions were performed at 25-, 50-, 120-m (with DJI Phantom 3 Advanced) and 350-m (with Trimble UX5) flight altitudes (above ground level, AGL). The imagery was processed with the state-of-the-art SfM (Structure-from-Motion) photogrammetry software. The ortho-mosaics and digital elevation models were generated from UAV-based photogrammetric and TLS-based data. GNSS- and TLS-based data were used as reference to calculate the accuracy of the UAV-based geodata. The UAV-results were assessed in 1D (points), 2D (areas) and 3D (volumes) based on the horizontal (X- and Y-directions) and vertical (Z-direction) differences. Various error measures, including the RMSE (Root Mean Square Error), ME (Mean Error) or MAE (Mean Average Error), and simple descriptive statistics were used to calculate the residuals. The comparison of the results is simplified by applying a normalization procedure commonly used in multi-criteria-decision-making analysis or visualizing offset. According to the results, low-altitude (25 and 50 m AGL) flights feature higher accuracy in the horizontal dimension (e.g., mean errors of 0.085 and 0.064 m, respectively) but lower accuracy in the Z-dimension (e.g., false positive volumes of 2402 and 1160 m3, respectively) compared to the higher-altitude flights (i.e., 120 and 350 m AGL). The accuracy difference with regard to the observed terrain heights are particularly striking, depending on the compared error measure, up to a factor of 40 (i.e., false positive values for 120 vs. 50 m AGL). This error is attributed to the “doming-effect”—a broad-scale systematic deformation of the reconstructed terrain surface, which is commonly known in SfM photogrammetry and results from inaccuracies in modeling the radial distortion of the camera lens. Within the scope of the study, the “doming-effect” was modeled as a functional surface by using the spatial differences and the results were indicated that the “doming-effect” increases inversely proportional to the flight altitude.

Description: Driving modes play vital roles in understanding the stochastic nature of a railway system and can support studies of automatic driving and capacity utilization optimization. Integrated trajectory data containing information such as GPS trajectories and gear changes can be good proxies in the study of driving modes. However, in the absence of labeled data, discovering driving modes is challenging. In this paper, instead of classical models (railway-specified feature extraction and classical clustering), we used five deep unsupervised learning models to overcome this difficulty. In these models, adversarial autoencoders and stacked autoencoders are used as feature extractors, along with generative adversarial network-based and Kullback–Leibler (KL) divergence-based networks as clustering models. An experiment based on real and artificial datasets showed the following: (i) The proposed deep learning models outperform the classical models by 27.64% on average. (ii) Integrated trajectory data can improve the accuracy of unsupervised learning by approximately 13.78%. (iii) The different performance rankings of models based on indices with labeled data and indices without labeled data demonstrate the insufficiency of people’s understanding of the existing modes. This study also analyzes the relationship between the discovered modes and railway carrying capacity.

Description: This paper presents a systematic literature review that reflects the current state of research in the field of algorithms and models for map generalization, the existing solutions for automatic (tactile) map generation, as well as good practices for designing spatial databases for the purposes of automatic map development. A total number of over 500 primary studies were screened in order to identify the most relevant research on automatic (tactile) map generation from the last decade. The reviewed papers revealed many existing solutions in the field of automatic map production, as well as algorithms (e.g., Douglas–Peucker, Visvalingam–Whyatt) and models (e.g., GAEL, CartACom) for data generalization that might be used to transform traditional spatial data into the haptic form, suitable for blind and visually impaired people. However, it turns out that a comprehensive solution for automatic tactile map generation does not exist.

Description: The stability and deformation behavior of high rock slopes depends on many factors, including geological structures, lithology, geomorphic processes, stress distribution, and groundwater regime. A comprehensive mapping program is, therefore, required to investigate and assess the stability of high rock slopes. However, slope steepness, rockfalls and ongoing instability, difficult terrain, and other safety concerns may prevent the collection of data by means of traditional field techniques. Therefore, remote sensing methods are often critical to perform an effective investigation. In this paper, we describe the application of field and remote sensing approaches for the characterization of rock slopes at various scale and distances. Based on over 15 years of the experience gained by the Engineering Geology and Resource Geotechnics Research Group at Simon Fraser University (Vancouver, Canada), we provide a summary of the potential applications, advantages, and limitations of varied remote sensing techniques for comprehensive characterization of rock slopes. We illustrate how remote sensing methods have been critical in performing rock slope investigations. However, we observe that traditional field methods still remain indispensable to collect important intact rock and discontinuity condition data.

Description: Overlay analysis is a common task in geographic computing that is widely used in geographic information systems, computer graphics, and computer science. With the breakthroughs in Earth observation technologies, particularly the emergence of high-resolution satellite remote-sensing technology, geographic data have demonstrated explosive growth. The overlay analysis of massive and complex geographic data has become a computationally intensive task. Distributed parallel processing in a cloud environment provides an efficient solution to this problem. The cloud computing paradigm represented by Spark has become the standard for massive data processing in the industry and academia due to its large-scale and low-latency characteristics. The cloud computing paradigm has attracted further attention for the purpose of solving the overlay analysis of massive data. These studies mainly focus on how to implement parallel overlay analysis in a cloud computing paradigm but pay less attention to the impact of spatial data graphics complexity on parallel computing efficiency, especially the data skew caused by the difference in the graphic complexity. Geographic polygons often have complex graphical structures, such as many vertices, composite structures including holes and islands. When the Spark paradigm is used to solve the overlay analysis of massive geographic polygons, its calculation efficiency is closely related to factors such as data organization and algorithm design. Considering the influence of the shape complexity of polygons on the performance of overlay analysis, we design and implement a parallel processing algorithm based on the Spark paradigm in this paper. Based on the analysis of the shape complexity of polygons, the overlay analysis speed is improved via reasonable data partition, distributed spatial index, a minimum boundary rectangular filter and other optimization processes, and the high speed and parallel efficiency are maintained.

Description: This paper focuses on the suitability of urban expansion in mountain areas against the background of accelerated urban development. Urbanization is accompanied by conflict and intense transformations of various landscapes, and is accompanied by social, economic, and ecological impacts. Evaluating the suitability of urban expansion (UE) and determining an appropriate scale is vital to solving urban environmental issues and realizing sustainable urban development. In mountain areas, the natural and social environments are different from those in the plains; the former is characterized by fragile ecology and proneness to geological disasters. Therefore, when evaluating the expansion of a mountain city, more factors need to be considered. Moreover, we need to follow the principle of harmony between nature and society according to the characteristics of mountain cities. Thus, when we evaluate the expansion of a mountain city, the key procedure is to establish a scientific evaluation system and explore the relationship between each evaluation factor and the urban expansion process. Taking Leshan (LS), China—a typical mountain city in the upper Yangtze River which has undergone rapid growth—as a case study, the logic minimum cumulative resistance (LMCR) model was applied to evaluate the suitability of UE and to simulate its direction and scale. The results revealed that: An evaluation system of resistance factors (ESRFs) was established according to the principle of natural and social harmony; the logic resistance surface (LRS) scientifically integrated multiple resistance factors based on the ESRF and a logic regression analysis. LRS objectively and effectively reflected the contribution and impact of each resistance factor to urban expansion. We found that landscape, geological hazards and GDP have had a great impact on urban expansion in LS. The expansion space of the mountain city is limited; the area of suitable expansion is only 23.5%, while the area which is unsuitable for expansion is 39.3%. In addition, it was found that setting up ecological barriers is an effective way to control unreasonable urban expansion in mountain cities. There is an obvious scale (grid size) effect in the evaluation of urban expansion in mountain cities; an evaluation of the suitable scale yielded the result of 90 m × 90 m. On this scale, taking the central district as the center, the urban expansion process will extend to the neighboring towns of Mianzhu, Suji, Juzi and Mouzi. Urban expansion should be controlled in terms of scale, especially in mountain cities. The most suitable urban size of LS is 132 km2.This would allow for high connectivity of urban-rural areas with the occupation of relatively few green spaces.

Description: Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization.

Description: With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS (location based service) applications have provided us with trajectory data services such as traffic flow statistics and user behavior pattern analyses. However, the storage and query efficiency of massive trajectory data are increasingly creating a bottleneck for these applications, especially for large-scale spatiotemporal query scenarios. To solve this problem, we propose a new spatiotemporal indexing method to improve the query efficiency of massive trajectory data. First, the method extends the GeoSOT spatial partitioning scheme to the time dimension and forms a global space–time subdivision scheme. Second, a novel multilevel spatiotemporal grid index, called the GeoSOT ST-index, was constructed to organize trajectory data hierarchically. Finally, a spatiotemporal range query processing method is proposed based on the index. We implement and evaluate the index in MongoDB. By comparing the range query efficiency and scalability of our index with those of the other two space–time composite indexes, we found that our approach improves query efficiency levels by approximately 40% and has better scalability under different data volumes.

Description: Single photon sensitive airborne Light Detection And Ranging (LiDAR) enables a higher area performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light potentially capable of penetrating clear shallow water. The technology is designed for large-area topographic mapping, which also includes the water surface. While the penetration capabilities of green lasers generally lead to underestimation of the water level heights, we specifically focus on the questions of whether Single Photon LiDAR (i) is less affected in this respect due to the high receiver sensitivity, and (ii) consequently delivers sufficient water surface echoes for precise high-resolution water surface reconstruction. After a review of the underlying sensor technology and the interaction of green laser light with water, we address the topic by comparing the surface responses of actual Single Photon LiDAR and Multi-Photon Topo-Bathymetric LiDAR datasets for selected horizontal water surfaces. The anticipated superiority of Single Photon LiDAR could not be verified in this study. While the mean deviations from a reference water level are less than 5 cm for surface models with a cell size of 10 m, systematic water level underestimation of 5–20 cm was observed for high-resolution Single Photon LiDAR based water surface models with cell sizes of 1–5 m. Theoretical photon counts obtained from simulations based on the laser-radar equation support the experimental data evaluation results and furthermore confirm the feasibility of Single Photon LiDAR based high-resolution water surface mapping when adopting specifically tailored flight mission parameters.

Description: Accurately and precisely knowing the location of the vehicle is a critical requirement for safe and successful autonomous driving. Recent studies suggest that error for map-based localization methods are tightly coupled with the surrounding environment. Considering this relationship, it is therefore possible to estimate localization error by quantifying the representation and layout of real-world phenomena. To date, existing work on estimating localization error have been limited to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven map evaluation factors were defined for 2D geographic information in a vector format, and random forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo. In the best model, the results show that it is possible to estimate autonomous vehicle localization error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm.

Description: Urban greenspace can provide physical and mental health benefits to residents, potentially reducing health inequalities associated with socioeconomic deprivation. The distribution of urban greenspace is an important social justice issue, and consequently is increasingly studied. However, there is little consistency between studies in terms of methods and definitions. There is no consensus on what comprises the most appropriate geographic units of analysis or how to capture residents’ experience of their neighbourhood, leading to the possibility of bias. Several complementary aspects of distribution equity have been defined, yet few studies investigate more than one of these. There are also alternative methods for measuring each aspect of distribution. All of these can lead to conflicting conclusions, which we demonstrate by calculating three aspects of equity for two units of aggregation and three neighbourhood sizes for a single study area. We make several methodological recommendations, including taking steps to capture the relevant neighbourhood as experienced by residents accurately as possible, and suggest that using small-area aggregations may not result in unacceptable levels of information loss. However, a consideration of the local context is critical both in interpreting individual studies and understanding differing results.

Description: The grouping of parcel data based on proximity is a pre-processing step of GIS and a key link of urban structure recognition for regional function discovery and urban planning. Currently, most literature abstracts parcels into points and clusters parcels based on their attribute similarity, which produces a large number of coarse granularity functional regions or discrete distribution of parcels that is inconsistent with human cognition. In this paper, we propose a novel parcel grouping method to optimise this issue, which considers both the urban morphology and the urban functional connectivity. Infiltration behaviours of urban components provide a basis for exploring the correlation between morphology mechanism and functional connectivity of urban areas. We measured the infiltration behaviours among adjacent parcels and concluded that the occurrence of infiltration behaviours often appears in the form of groups, which indicated the practical significance of parcel grouping. Our method employed two parcel morphology indicators: the similarity of the line segments and the compactness of the distribution. The line segment similarity was used to establish the adjacent relationship among parcels and the compactness was used to optimise the grouping result in obtain a satisfactory visual expression. In our study, constrained Delaunay triangulation, Hausdorff distance, and graph theory were employed to construct the proximity, delineate the parcel adjacency matrix, and implement the grouping of parcels. We applied this method for grouping urban parcel data of Beijing and verified the rationality of grouping results based on the quantified results of infiltration behaviours. Our method proved to take a good account of infiltration behaviours and satisfied human cognition, compared with a k-means++ method. We also presented a case using Xicheng District in Beijing to demonstrate the practicability of the method. The result showed that our method obtained fine-grained groups while ensuring functional regions-integrity.

Description: Certain historical works of civil engineering should be preserved as heritage monuments and when possible should continue serving the function they were designed for. Old stone bridges could be sustainably maintained but their conservation requires accurate documentation. In this study, we have scanned Ízbor bridge (1860) in Spain, and to facilitate conservation, we have modeled the ancient bridge using BIM (building information modeling). We propose a method and a model for this kind of bridge to be used as a reference for similar heritage monuments. Ízbor bridge modeled in this way will be useful for government planning and conservation agencies.

Description: Deriving 3D urban development patterns is necessary for urban planners to control the future directions of 3D urban growth considering the availability of infrastructure or being prepared for fundamental infrastructure. Urban metrics have been used so far for quantification of landscape and land-use change. However, these studies focus on the horizontal development of urban form. Therefore, questions remain about 3D growth patterns. Both 3D data and appropriate 3D metrics are fundamentally required for vertical development pattern extraction. Airborne light detection and ranging (Lidar) as an advanced remote-sensing technology provides 3D data required for such studies. Processing of airborne lidar to extract buildings’ heights above a footprint is a major task and current automatic algorithms fail to extract such information on vast urban areas especially in hilly sites. This research focuses on proposing new methods of extraction of ground points in hilly urban areas using autocorrelation-based algorithms. The ground points then would be used for digital elevation model generation and elimination of ground elevation from classified buildings points elevation. Technical novelties in our experimentation lie in choosing a different window direction and also contour lines for the slant area, and applying moving windows and iterating non-ground extraction. The results are validated through calculation of skewness and kurtosis values. The results show that changing the shape of windows and their direction to be narrow long squares parallel to the ground contour lines, respectively, improves the results of classification in slant areas. Four parameters, namely window size, window shape, window direction and cell size are empirically chosen in order to improve initial digital elevation model (DEM) creation, enhancement of the initial DEM, classification of non-ground points and final creation of a normalised digital surface model (NDSM). The results of these enhanced algorithms are robust for generating reliable DEMs and separation of ground and non-ground points in slant urban scenes as evidenced by the results of skewness and kurtosis. Offering the possibility of monitoring urban growth over time with higher accuracy and more reliable information, this work could contribute in drawing the future directions of 3D urban growth for a smarter urban growth in the Smart Cities paradigm.

Description: With the increasing use and complexity of urban natural gas pipelines, the occurrence of accidents owing to leakage, fire, explosion, etc., has increased. Based on Quantitative Risk Analysis (QRA) models and Geographic Information System (GIS) technology, we put forward a quantitative risk simulation model for urban natural gas pipeline, combining with a multi-level grid-based pre-warning model. We develop a simulation and pre-warning model named QRA-Grid, conducting fire and explosion risk assessment, presenting the risk by using a grid map. Experiments show that by using the proposed method, we can develop a fire and explosion accident pre-warning model for gas pipelines, and effectively predict areas in which accidents will happen. As a result, we can make a focused and forceful policy in areas which have some potential defects in advance, and even carry out urban planning once more, rebuilding it to prevent the risk.

Description: The construction of transport infrastructure is often preceded by an environmental impact assessment procedure, which should identify amphibian breeding sites and migration routes. However, the assessment is very difficult to conduct because of the large number of habitats spread out over a vast expanse, and the limited amount of time available for fieldwork. We propose utilizing local environmental variables that can be gathered remotely using only GIS systems and satellite images together with machine learning methods. In this article, we introduce six new and easily extractable types of environmental features. Most of the features we propose can be easily obtained from satellite imagery and spatial development plans. The proposed feature space was evaluated using four machine learning algorithms, namely: a C4.5 decision tree, AdaBoost, random forest and gradient-boosted trees. The obtained results indicated that the proposed feature space facilitated prediction and was comparable to other solutions. Moreover, three of the new proposed features are ranked most important; these are the three dominant properties of the surroundings of water reservoirs. One of the new features is the percentage access from the edges of the reservoir to open areas, but it affects only a few species. Furthermore, our research confirmed that the gradient-boosted trees were the best method for the analyzed dataset.

Description: With the wide use of web technologies, service-oriented architecture (SOA), and cloud computing, more and more geographical information systems are served as GIServices. Under such circumstance, quality of geographic information services (QoGIS) has emerged as an important research topic of geoinformatics. However, it is not easy to understand the field since QoGIS has no formal standards, which is not only in regard to server-side performance and capabilities, but is also related with the quality of experience (QoE) during user interaction with GIServices. In this paper, we compare quality of service (QoS) and QoGIS research to understand the uniqueness of QoGIS. A conceptual framework is proposed to organize and interpret QoGIS research from the perspective of quality modeling, acquisition, and application, and we discuss the status, limitations, and future directions of this area. Overall, our analysis shows that new quality metrics will evolve from existing metrics to match the needs in concrete QoGIS applications, and user preferences need to be considered in quality modeling for GIServices. We discuss three approaches for the provision of QoGIS information and find that user feedback mining is an important supplementary source of quality information. Gaps between QoS and QoGIS research suggest that the GIService performance enhancement must not only consider the unique features of spatial data models and algorithms, but also system architecture, deployment, and user spatiotemporal access behaviors. Advanced service selection algorithms must be introduced to tackle the quality optimization problems of geoprocessing workflow planning. Moreover, a QoGIS-aware GIServices framework must be established to facilitate and ensure GISerivce discovery and interaction. We believe this bibliographic review provides a helpful guide for GIS researchers.

Description: Social media platforms such as Twitter are extensively used for expressing and exchanging thoughts, opinions, ideas, and feelings, i.e., reactions concerning a topic or an event. Factual information about an event to which people are reacting can be obtained from different types of (geo-)sensors, official authorities, or the public press. However, these sources hardly reveal the emotional or attitudinal impact of events on people, which is, for example, reflected in their reactions on social media. Two approaches that utilize emojis are proposed to obtain the sentiment and emotions contained in social media reactions. Subsequently, these two approaches, along with visualizations that focus on space, time, and topic, are applied to Twitter reactions in the example case of Brexit.

Description: Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.

Description: Structural measures for retaining and distributing water—i.e., reservoirs, flood retention and power plants—play a key role to protect and feed a growing world population in a rapidly changing climate. In this work, we introduce an automated method to detect potential reservoir or retention area locations in digital terrain models. In this context, a potential reservoir is a larger terrain form that can be turned into an actual reservoir by constructing a dam. Based on contour lines derived from terrain models, potential reservoirs are found within a predefined range of dam lengths, and the locally optimal ones are then extracted. Our method is to be applied in the very early stages of project planning and for area-wide potential analysis. Tests in a 100 km2 study area bring promising results, but also show a certain sensitivity regarding terrain model quality and resolution. In total, 250–300 candidate polygons with a total volume of more than 6 million m3 were found. In order to facilitate further processing, these are stored as a GIS vector dataset.

Description: In a point set in dimension superior to 1, the statistical distribution of the number of pairs of points as a function of distance between the points of the pair is not uniform. This distribution is not considered in a large number of classic methods based on spatially weighted means used in spatial analysis, such as spatial autocorrelation indices, kernel interpolation methods, or spatial modeling methods (autoregressive, or geographically weighted). It has a direct impact on the calculations and the results of indices and estimations, and by not taking into account this distribution of the distances, spatial analysis calculations can be biased. In this article, we introduce a “spatial standardization”, which corrects and adjusts the calculations with respect to the distribution of point pairs distances. As an example, we apply this correction to the calculation of spatial autocorrelation indices (Moran and Geary indices) and to trend surface calculation (by spatial kernel interpolation) on the results of the 2017 French presidential election.

Description: Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ecology and human life in Australia. This study proposes a prototype of fire propagation prediction as an extension of preceding research; this system is called “Cloud computing based bushfire prediction”, the computational performance of which is expected to be about twice that of the traditional client-server (CS) model. As the first step in the modelling approach, this prototype focuses on the prediction of fire propagation. The direction of fire is limited in regular grid approaches, such as cellular automata, due to the shape of the uniformed grid, while irregular grids are freed from this constraint. In this prototype, fire propagation is computed from a centroid regardless of grid shape to remove the above constraint. Additionally, the prototype employs existing fire indices, including the Grassland Fire Danger Index (GFDI), Forest Fire Danger Index (FFDI) and Button Grass Moorland Fire Index (BGML). A number of parameters, such as Digital Elevation Model (DEM) and forecast weather data, are prepared for use in the calculation of the indices above. The fire study area is located around Lake Mackenzie in the central north of Tasmania where a fire burnt approximately 247.11 km 2 in January 2016. The prototype produces nine different prediction results with three polygon configurations, including Delaunay Triangulation, Square and Voronoi, using three different resolutions: fine, medium and coarse. The Delaunay Triangulation, which has the greatest number of adjacent grids among three shapes of polygon, shows the shortest elapsed time for spread of fire compared to other shapes. The medium grid performs the best trade-off between cost and time among the three grain sizes of prediction polygons, and the coarse size shows the best cost-effectiveness. A staging approach where coarse size prediction is released initially, followed by a medium size one, can be a pragmatic solution for the purpose of providing timely evacuation guidance.

Description: This article aims at testing the possibilities of applying hierarchical spatial autoregressive models to create land value maps in urbanized areas. The use of HSAR (Hierarchical Spatial Autoregressive) models for spatial differentiation of prices in the property market supports the multilevel diagnosis of the structure of this phenomenon, taking into account the effect of spatial interactions. The article applies a two-level hierarchical spatial autoregressive model, which will permit the evaluation of interactions and control spatial heterogeneity at two levels of spatial aggregation (general and detailed). The results of the research include both the evaluation of the impact of location on prices (taking into account non-spatial factors) and the creation of the average land price map, taking into consideration the spatial structure of the city. In empirical studies, the HSAR model was compared with classic LM (Linear Model), HLM (Hierarchical Linear Model), and SAR (Spatial Autoregressive) models to perform comparative analyses of the results.

Description: So-called prismatic 3D building models, following the level-of-detail (LOD) 1 of the OGC City Geography Markup Language (CityGML) standard, are usually generated automatically by combining building footprints with height values. Typically, high-resolution digital elevation models (DEMs) or dense LiDAR point clouds are used to generate these building models. However, high-resolution LiDAR data are usually not available with extensive coverage, whereas globally available DEM data are often not detailed and accurate enough to provide sufficient input to the modeling of individual buildings. Therefore, this paper investigates the possibility of generating LOD1 building models from both volunteered geographic information (VGI) in the form of OpenStreetMap data and remote sensing-derived geodata improved by multi-sensor and multi-modal DEM fusion techniques or produced by synthetic aperture radar (SAR)-optical stereogrammetry. The results of this study show several things: First, it can be seen that the height information resulting from data fusion is of higher quality than the original data sources. Secondly, the study confirms that simple, prismatic building models can be reconstructed by combining OpenStreetMap building footprints and easily accessible, remote sensing-derived geodata, indicating the potential of application on extensive areas. The building models were created under the assumption of flat terrain at a constant height, which is valid in the selected study area.

Description: Immigrants and natives are generally comparable in committing violent crimes in many Western cities. However, little is known about spatial differences between internal migrant offenders and native offenders in committing violence in contemporary urban China. To address this gap, this research aims to explore spatial variation in violent crimes committed by migrant and native offenders, and examine different effects of ambient population, crime attractors, crime generators, and offender anchor points on these crimes. Offender data, mobile phone data, and points-of-interest (POI) data are combined to explain the crime patterns of these offenders who committed offenses and were arrested from 2012 to 2016 in a large Chinese city by using box maps and negative binomial regression models. It is demonstrated that migrant and native violent crimes vary enormously across space. Ambient population is only positively related to migrant violent crimes. Crime attractors and generators have more significant and stronger correlations with migrant violent crimes, while offender anchor points have a stronger association with native violent crimes. The results reveal that migrant offenders tend to be attracted by larger amounts of people and more affected by crime attractors and generators than native offenders.

Description: This article proposes the use of a multiscale and multisensor approach to collect and model three-dimensional (3D) data concerning wide and complex areas to obtain a variety of metric information in the same 3D archive, which is based on a single coordinate system. The employment of these 3D georeferenced products is multifaceted and the fusion or integration among different sensors’ data, scales, and resolutions is promising, and it could be useful in the generation of a model that could be defined as a hybrid. The correct geometry, accuracy, radiometry, and weight of the data models are hereby evaluated when comparing integrated processes and results from Terrestrial Laser Scanner (TLS), Mobile Mapping System (MMS), Unmanned Aerial Vehicle (UAV), and terrestrial photogrammetry, while using Total Station (TS) and Global Navigation Satellite System (GNSS) for topographic surveys. The entire analysis underlines the potentiality of the integration and fusion of different solutions and it is a crucial part of the ‘Torino 1911’ project whose main purpose is mapping and virtually reconstructing the 1911 Great Exhibition settled in the Valentino Park in Turin (Italy).

Description: With the rapid development of sharing bicycles, unreasonable dispatching methods are likely to cause a series of issues, such as resource waste and traffic congestion in the city. In this paper, a new dynamic scheduling method is proposed, named Tri-G, so as to solve the above problems. First of all, the whole visualization information of bike stations was built based on a Spatio-Temporal Graph (STG), then Gaussian Mixture Mode (GMM) was used to group individual stations into clusters according to their geographical locations and transition patterns, and the Gradient Boosting Regression Tree (GBRT) algorithm was adopted to predict the number of bikes inflow/outflow at each station in real time. This paper used New York’s bicycle commute data to build global STG visualization information to evaluate Tri-G. Finally, it is concluded that Tri-G is superior to the methods in control groups, which can be applied to various geographical scenarios. In addition, this paper also discovered some human mobility patterns as well as some rules, which are helpful for governments to improve urban planning.

Description: The timely sharing and interoperation of multi-source cross-sectoral information is an important issue for a Geographic Information System (GIS). To study this issue, a real-time and open GIS model called GeoSensor is proposed in this work. GeoSensor integrates the real-time GIS model, real-time computation framework, and Open Geospatial Consortium services. This paper illustrates the system architecture and the implementation methods of the GeoSensor. One of the methods developed is the conceptual mapping of a real-time GIS data model to open GIS models and services and a real-time computation framework. The other method developed is the integration of open GIS services, a real-time computation framework, and hybrid databases. The GeoSensor was tested in a case study of building a smart river. In the case study, a comprehensive sensor web was constructed and integrated with the real-time information on rainfall, beacon, channel, sediment, and water levels derived from space-based sensors, air-borne sensors, and underground sensors from multiple sectors in the Yangtze River basin. The GeoSensor manages the comprehensive sensor web with 32 types of sensors and more than 10 billion observation records. Three application systems were developed based on the GeoSensor to manage flood control, hydropower production, and navigation of the Yangtze River. The results of the three application systems show that the real-time and open system improves the management efficiency of the Yangtze River. This system is promising for managing smart rivers.

Description: Social media data have been used to improve geographic situation awareness in the past decade. Although they have free and openly availability advantages, only a small proportion is related to situation awareness, and reliability or trustworthiness is a challenge. A credibility framework is proposed for Twitter data in the context of disaster situation awareness. The framework is derived from crowdsourcing, which states that errors propagated in volunteered information decrease as the number of contributors increases. In the proposed framework, credibility is hierarchically assessed on two tweet levels. The framework was tested using Hurricane Harvey Twitter data, in which situation awareness related tweets were extracted using a set of predefined keywords including power, shelter, damage, casualty, and flood. For each tweet, text messages and associated URLs were integrated to enhance the information completeness. Events were identified by aggregating tweets based on their topics and spatiotemporal characteristics. Credibility for events was calculated and analyzed against the spatial, temporal, and social impacting scales. This framework has the potential to calculate the evolving credibility in real time, providing users insight on the most important and trustworthy events.

Description: The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) provides satellite-based climate data records of essential climate variables of the energy budget and water cycle. The data records are generally distributed in NetCDF format. To simplify the preparation, analysis, and visualization of the data, CM SAF provides the so-called CM SAF R Toolbox. This is a collection of R-based tools, which are optimized for spatial data with longitude, latitude, and time dimension. For analysis and manipulation of spatial NetCDF-formatted data, the functionality of the cmsaf R-package is implemented. This R-package provides more than 60 operators. The visualization of the data, its properties, and corresponding statistics can be done with an interactive plotting tool with a graphical user interface, which is part of the CM SAF R Toolbox. The handling, functionality, and visual appearance are demonstrated here based on the analysis of sunshine duration in Europe for the year 2018. Sunshine duration in Scandinavia and Central Europe was extraordinary in 2018 compared to the long-term average.

Description: Infrastructure management has become a serious problem in many cities. However, the monitoring of daily urban infrastructure requires active feedback, not only by municipal government officers, but also by citizens. In this study, we analyzed Chiba City’s ‘Chiba-repo’ platform to measure citizen feedback and collaboration in urban infrastructure maintenance. We compiled data on over 40,000 citizen-generated reports of infrastructure issues during the period from September 2014 to December 2016 through the Chiba-repo platform and analyzed the geographical distribution and text mining by categorizing the reports. The most frequent report was about road issues, representing 93.8% of the total. As a result, many reports were received from citizens from a time-consuming report like light repairs (average 24.4 days); also, road issues were revealed to be a major town problem. On the other hand, the unsolved issue rate is lower (3.7%) compared with telephone correspondence and counter contact, since posting through the web application allows for a detailed report that includes position information and photographs. The research also predicted that many infrastructure problems would occur on narrow roads and in areas with many elderly people, and that road issue reports are regularly needed in areas that cannot be found or patrolled by administrative staff.