ALBERT

Beschreibung: Since reinforced concrete (RC) buildings have long service life, cutout openings are generally needed in their load-bearing walls as a sustainable solution in order to meet new requirements of their users. However, the cutout openings decrease the load-bearing capacity of the walls, which may result in the failure of the buildings. In this paper, we investigate the possibility of making a door opening in a load-bearing RC wall of an existing building in Gävle in Sweden. The wall studied in the current paper rests on two individual supports at its two ends; thus, it is considered as a deep beam. However, it is called an examined wall (EW) here. The StruSoft FEM-Design software is used in this study to model, analyze, and design the building based on the Eurocodes and Swedish national annex. The potential need for the EW to be strengthened when the cutout opening is made is also evaluated. It is concluded that strengthening the EW with cutout opening is needed. Different strengthening solutions are proposed for the EW. Moreover, the situation of the EW with the solutions is assessed with regard to the utilization ratio, deflection, and weight. Consequently, it is demonstrated that the proposed strengthening solutions function well for the EW.

Beschreibung: Pinned base plate connections are the most common base connection used in low-rise steel buildings. In this research, an extensive parametric study is performed using the Finite Element (FE) software Abaqus to determine the elastic rotational stiffness, moment resistance, and energy absorption of the pinned base plate connection connected to a reinforced concrete footing and subjected to an eccentric axial load. The developed FE model is validated using experimental results from the literature. Moreover, an intensive parametric study is conducted to understand the behavior of these connections better. The investigated parameters include the base plate thickness, anchor bolt diameter, and arrangement and number of bolts. The most effective parameters that affect the elastic rotational stiffness and moment resistance of pinned base connections are the anchor bolt arrangement and diameter. The maximum increase in the rotational stiffness was 53% for the anchor bolt diameter of 30 mm when the base plate thickness increased from 12 mm to 30 mm. Based on the base plate thickness, the moment resistance is improved by 150–260% when the bolt diameter increases from 12 mm to 30 mm.

Beschreibung: This state-of-the-art review was aimed to conduct a comprehensive literature survey to summarize experiences of crumb rubber (CR) application in concrete within the last 30 years. It shows that certain gaps prevent obtaining a coherent overview of both mechanical behaviour and environmental impact of crumb rubber concrete (CRC) to object to the stereotypes which prevent to use of CR in concrete in the construction industry. Currently, four major barriers can be distinguished for a successful CR application in the concrete industry: (1) the cost of CR recycling, (2) mechanical properties reduction, (3) insufficient research about leaching criteria and ecotoxicological risks and (4) recyclability of CRC. The application of CR in concrete has certainly its advantages and in general cannot be ignored by the construction industry. CR can be applied, for example, as an alternative material to replace natural aggregates and CRC can be used as recycled concrete aggregates (RCA) in the future. A certain diversity for the CR application can be introduced in a more efficient way when surface treatment and concrete mix design optimization are properly developed for each type of CR application in concrete for possible field applications. The role of CRC should not be limited to structures that are less dependent on strength.

Beschreibung: Projective transformation of spheres onto images produce ellipses, whose centers do not coincide with the projected center of the sphere. This results in an eccentricity error, which must be treated in high precision metrology. This article provides closed formulations for modeling this error in images to enable 3-dimensional (3D) reconstruction of the center of spherical objects. The article also provides a new direct robust method for detecting spherical pattern in point clouds. It was shown that the eccentricity error in an image has only one component in the direction of the major axis of the ellipse. It was also revealed that the eccentricity is zero if and only if the center of the projected sphere lies on the camera’s perspective center. The effectiveness of the robust sphere detection and the eccentricity error modeling method was evaluated on simulated point clouds of spheres and real-world images, respectively. It was observed that the proposed robust sphere fitting method outperformed the popular M-estimator sample consensus in terms of radius and center estimation accuracy by a factor of 13, and 14 on average, respectively. Using the proposed eccentricity adjustment, the estimated 3D center of the sphere using modeled eccentricity was superior to the unmodeled case. It was also observed that the accuracy of the estimated 3D center using modeled eccentricity continuously improved as the number of images increased, whereas the unmodeled eccentricity did not show improvements after eight image views. The results of the investigation show that: (i) the proposed method effectively modeled the eccentricity error, and (ii) the effects of eliminating the eccentricity error in the 3D reconstruction become even more pronounced in a larger number of image views.

Beschreibung: In watershed mountain basins, affected in the last decades by strong rainfall events, the role of debris-flow and debris flood processes was investigated. Morphometric parameters have proven to be useful first-approximation indicators in discriminating those processes, especially in large areas of investigation. Computation of morphometric parameters in 19 watershed mountain basins of the western side valley of the Vallo di Diano intermontane basin (southern Italy) was carried out. This procedure was integrated by a semi-automatic elaboration of the potential susceptibility to debris flows, using Flow-R modelling. This software, providing an empirical model of the preliminary susceptibility assessment at a regional scale, was applied in many countries of the world. The implementation of Flow-R modelling requires a GIS application and some thematic base maps extracted using DEMs analysis. A 5-meter-resolution DEM has been used in order to produce the susceptibility maps of the whole study area, and the results are compared and discussed with the real debris flow/flood events that occurred in 1993, 2005, 2010, and 2017 in the studied area. The results have provided a good reliability of Flow-R modelling within small catchment mountain basins.

Beschreibung: Deformation monitoring has been brought to the fore and extensively studied in recent years. Global Navigation Satellite System Reflectometry (GNSS-R) techniques have so far been developed in deformation estimation applications, which however, are subject to the influence of mobile satellites. Rather than compensating for the path delay variations caused by mobile satellites, adopting Beidou geostationary Earth orbit (GEO) satellites as transmitters directly eliminates the satellite-motion-induced phase error and thus provides access to stable phase information. This paper presents a novel deformation monitoring concept based on GNSS-R utilizing Beidou GEO satellites. The geometrical properties of the GEO-based bistatic GNSS radar system are explored to build a theoretical connection between deformation quantity and the echo carrier phases. A deformation retrieval algorithm is proposed based on the supporting software receiver, thus allowing echo carrier phases to be extracted and utilized in deformation retrieval. Two field validation experiments are conducted by constructing passive bistatic radars with reflecting plates and ground receiver. Utilizing the proposed algorithm, the experimental results suggested that the GEO-based GNSS reflectometry can achieve deformation estimations with an accuracy of around 1 cm when the extracted phases does not exceed one complete cycle, while better than 3 cm when considering the correct integer number of phase cycles. Consequently, based on the passive bistatic radar system, the potential of achieving continuous, low-cost deformation monitoring makes this novel technique noteworthy.

Beschreibung: Container-based lightweight buildings offer a high ecologic and economic potential when they are designed as nearly zero-energy container buildings (NZECBs). Thus, they are relevant to energy transition in achieving an almost climate-neutral building stock. This paper describes and applies design strategies for suitable building concepts and energy systems to be used in NZECBs for different climates. Therefore, different applications in representative climatic zones were selected. Initially, the global climate zones were characterized and analyzed with regard to their potential for self-sufficiency and renewable energies in buildings. The design strategies were further developed and demonstrated for three cases: a single-family house in Sweden, a multi-family house in Germany, and a small school building in rural Ethiopia. For each case, design guidelines were derived and building concepts were developed. On the basis of these input data, various energy concepts were developed in which solar and wind energy, as well as biomass, were integrated as renewable energy sources. All the concepts were simulated and analyzed with the Polysun® software. The various approaches were compared and evaluated, particularly with regard to energy self-sufficiency. Self-sufficiency rates up to 80% were achieved. Finally, the influence of different climate zones on the energy efficiency of the single-family house was studied as well as the influence of the size of battery storage and insulation.

Beschreibung: The paper presents a comparative analysis of recent collision avoidance and real-time path planning algorithms for ships. Compared methods utilize radar remote sensing for target ships detection. Different recently introduced approaches are briefly described and compared. An emphasis is put on input data reception using a radar as a remote sensing device applied in order to detect moving obstacles such as encountered ships. The most promising methods are highlighted and their advantages and limitations are discussed. Concluding remarks include proposals of further research directions in the development of collision avoidance methods utilizing radar remote sensing.

Beschreibung: Suffering from hardware phase biases originating from satellites and the receiver, precise point positioning (PPP) requires a long convergence time to reach centimeter coordinate accuracy, which is a major drawback of this technique and limits its application in time-critical applications. Ambiguity resolution (AR) is the key to a fast convergence time and a high-precision solution for PPP technology and PPP AR products are critical to implement PPP AR. Nowadays, various institutions provide PPP AR products in different forms with different strategies, which allow to enable PPP AR for Global Positioning System (GPS) and Galileo or BeiDou Navigation System (BDS). To give a full evaluation of PPP AR performance with various products, this work comprehensively investigates the positioning performance of GPS-only and multi-GNSS (Global Navigation Satellite System) combination PPP AR with the precise products from CNES, SGG, CODE, and PRIDE Lab using our in-house software. The positioning performance in terms of positioning accuracy, convergence time and fixing rate (FR) as well as time to first fix (TTFF), was assessed by static and kinematic PPP AR models. For GPS-only, combined GPS and Galileo PPP AR with different products, the positioning performances were all comparable with each other. Concretely, the static positioning errors can be reduced by 21.0% (to 0.46 cm), 52.5% (to 0.45 cm), 10.0% (to 1.33 cm) and 21.7% (to 0.33 cm), 47.4% (to 0.34 cm), 9.5% (to 1.16 cm) for GPS-only and GE combination in north, east, up component, respectively, while the reductions are 20.8% (to 1.13 cm), 42.9% (to 1.15 cm), 19.9% (to 3.4 cm) and 20.4% (to 0.72 cm), 44.1% (to 0.66 cm), 10.1% (to 2.44 cm) for kinematic PPP AR. Overall, the positioning performance with CODE products was superior to the others. Furthermore, multi-GNSS observations had significant improvements in PPP performance with float solutions and the TTFF as well as the FR of GPS PPP AR could be improved by adding observations from other GNSS. Additionally, we have released the source code for multi-GNSS PPP AR, anyone can freely access the code and example data from GitHub.

Beschreibung: This work aims to study the influence of using construction and demolition waste in the replacement of coarse and fine aggregate to produce recycled aggregate concrete (RAC). A moderate compressive strength concrete made with usual fine and coarse aggregate was used as a benchmark material. Compressive and split tensile tests were performed using 120 cylindrical concrete specimens with 150 mm diameter and 300 mm length. Four-point flexural tests in reinforced beams made with conventional concrete and RAC were performed. The results obtained showed that the use of recycled fine aggregates, in both percentages of substitution investigated—50% and 100%— did not generate any deleterious influence on the values of compressive strength and split tensile strength of the RACs produced. Tin fact, the mechanical strengths of RACs produced with recycled fine aggregate were equal or higher than those from the reference concrete. The same behavior was not observed, however, when the recycled coarse aggregate was used. For this case, decreases in concrete mechanical strengths were observed, especially in compressive strength, with values around 35% lower when compared to the reference concrete. Tensile mechanical tests results confirmed the excellent behavior of all RACs made with replacement of usual fine aggregates by recycled. Bending tests performed in reinforced RAC beams had as objective to evaluate the deformation profile of the beams. The obtained results showed that RAC beams with full replacement of usual fine aggregate by the recycled aggregates have presented little changes in the global behavior, an aspect that encourages its use.

Beschreibung: Even though, an innovative inorganic family of geopolymer concretes are eye-catching potential building materials, it is quite essential to comprehend the fire and thermal resistance of these structural materials at a very high temperature and also when experiencing fire with a view to make certain not only the safety and security of lives and properties but also to establish them as more sustainable edifice materials for future. The experimental and field observations of degree of cracking, spalling and loss of strength within the geopolymer concretes subsequent to exposure at elevated temperature and incidences of occurrences of disastrous fires extend an indication of their resistance against such severely catastrophic conditions. The impact of heat and fire on mechanical attributes viz., mechanical-compressive strength, flexural behavior, elastic modulus; durability—thermal shrinkage; chemical stability; the impact of thermal creep on compressive strength; and microstructure properties—XRD, FTIR, NMR, SEM as well as physico-chemical modifications of geopolymer composites subsequent to their exposures at elevated temperatures is reviewed in depth. The present scientific state-of-the-art review manuscript aimed to assess the fire and thermal resistance of geopolymer concrete along with its thermo-chemistry at a towering temperature in order to introduce this novel, most modern, user and eco-benign construction materials as potentially promising, sustainable, durable, thermal and fire-resistant building materials promoting their optimal and apposite applications for construction and infrastructure industries.

Beschreibung: TouchTerrain is a simple-to-use web application that makes creating 3D printable terrain models from anywhere on the globe accessible to a wide range of users, from people with no GIS expertise to power users. For coders, a Python-based standalone version is available from the open-source project's GitHub repository. Analyzing 18 months of web analytics gave us a preliminary look at who is using the TouchTerrain web application and what their models are used for; and to map out what terrains on the globe they chose to 3D print. From July 2019 to January 2021, more than 20,000 terrain models were downloaded. Models were created for many different use cases, including education, research, outdoor activities and crafting mementos. Most models were realized with 3D printers, but a sizable minority used CNC machines. Our own experiences with using 3D printed terrain in a university setting have been very positive so far. Anecdotal evidence points to the strong potential for 3D printed terrain models to provide significant help with specific map-related tasks. For the introductory geology laboratory, 3D printed models were used as a form of “training wheels” to aid beginning students in learning to read contour maps, which are still an important tool for geology.

Beschreibung: The basic urban tissue of medieval European cities consisted of brick townhouses. In the cities of northern Europe, these tenements were characterised by a block based on an elongated rectangular plan, covered with a gable roof with a ridge oriented perpendicularly to the street. The side walls of the tenement house were common for both neighbours and constituted a basic structural element. The gable façades were not loaded with ceilings, providing freedom in shaping them. The aim of this work is to determine the reasons why this method of shaping tenement houses in historical city centres has survived to the present day, becoming an archetype for contemporary architectural and construction solutions, despite the passage of time, numerous historical events, war damage, changing architectural styles, fashions and building techniques and technologies. The historical centre of Gdańsk has become the research material in this paper, where by means of such methods as historical source material analysis (iconographic), observation (operationalisation of preserved historical objects), comparative analysis of completed contemporary investments, and 3D modelling of structural systems, an attempt has been made to determine the main factors determining contemporary architectural and structural solutions. The reason for the extraordinary durability of this type of construction model can be found in the enormous rationality and efficiency of this solution. It allows for very intensive use of land, easy access of all front elevations to the main communication routes, cheapness of construction resulting from small spans and use of common structural walls for the neighbouring buildings, ease of shaping gable elevations, and fire safety. Aesthetic considerations are probably also important here, although it should be assumed that their significance began to grow only in the second half of the 19th century. However, it seems that the most important factor which made the model of the mediaeval bourgeois tenement house become an archetype for contemporary architectural and construction solutions is the timeless message contained in this model, a specific code allowing it to be unambiguously identified as a form of urban house – a place of safe living and at the same time a visible sign of the rich history of European cities, an element creating their cultural and spatial identity, a component of the living, constantly transforming urban fabric.

Beschreibung: The development of generalisation (simplification) methods for the geometry of features in digital cartography in most cases involves the improvement of existing algorithms without their validation with respect to the similarity of feature geometry before and after the process. It also consists of the assessment of results from the algorithms, i.e., characteristics that are indispensable for automatic generalisation. The preparation of a fully automatic generalisation for spatial data requires certain standards, as well as unique and verifiable algorithms for particular groups of features. This enables cartographers to draw features from these databases to be used directly on the maps. As a result, collected data and their generalised unique counterparts at various scales should constitute standardised sets, as well as their updating procedures. This paper proposes a solution which consists in contractive self-mapping (contractor for scale s = 1) that fulfils the assumptions of the Banach fixed-point theorem. The method of generalisation of feature geometry that uses the contractive self-mapping approach is well justified due to the fact that a single update of source data can be applied to all scales simultaneously. Feature data at every scale s 〈 1 are generalised through contractive mapping, which leads to a unique solution. Further generalisation of the feature is carried out on larger scale spatial data (not necessarily source data), which reduces the time and cost of the new elaboration. The main part of this article is the theoretical presentation of objectifying the complex process of the generalisation of the geometry of a feature. The use of the inherent characteristics of metric spaces, narrowing mappings, Lipschitz and Cauchy conditions, Salishchev measures, and Banach theorems ensure the uniqueness of the generalisation process. Their application to generalisation makes this process objective, as it ensures that there is a single solution for portraying the generalised features at each scale. The present study is dedicated to researchers concerned with the theory of cartography.

Beschreibung: In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such as water bodies. One of the current challenges is how to fuse the benefits of both to obtain more powerful classification capabilities. This study proposes a classification model based on random forest with the conditional random fields (CRF) for feature-level fusion classification using features extracted from polarized SAR and optical images. In this paper, feature importance is introduced as a weight in the pairwise potential function of the CRF to improve the correction rate of misclassified points. The results show that the dataset combining the two provides significant improvements in feature identification when compared to the dataset using optical or polarized SAR image features alone. Among the four classification models used, the random forest-importance_ conditional random fields (RF-Im_CRF) model developed in this paper obtained the best overall accuracy (OA) and Kappa coefficient, validating the effectiveness of the method.

Beschreibung: Detection of small-sized maritime targets is an important task for a marine surveillance radar. Recently, with the emergence of a marine surveillance radar system that has a narrow azimuth beamwidth and rapidly rotating antennas, the available dwell time for detecting a maritime target is usually very short. This short dwell time considerably degrades the performance of conventional detectors, especially those focusing on small-sized targets. In this paper, we propose an efficient detector for small-sized maritime targets to provide a reliable detection performance, even in short dwell times. The proposed scheme is based on a new joint metric, which results from the product of the magnitude and difference features in the Doppler spectra. We discriminate the target bins from sea clutter bins using a statistical discriminator based on the joint metric, whose probability density function follows the product distribution of standard gamma distributions. Compared to conventional detectors, the proposed scheme can provide a robust performance in terms of the average signal-to-clutter ratio as well as the detection rate, especially in shorter dwell times.

Beschreibung: As an approach with great potential, the interpretation of space-borne synthetic aperture radar (SAR) images has been applied for monitoring the dynamic evolution of the glacial lakes in recent years. Considering unfavorable factors, such as inherent topography-induced effects and speckle noise in SAR images, it is challenging to accurately map and track the dynamic evolution of the glacial lakes by using multi-temporal SAR images. This paper presents an improved neighborhood-based ratio method utilizing a time series of SAR images to identify the boundaries of the glacial lakes and detect their spatiotemporal changes. The proposed method was applied to monitor the dynamic evolution of the two glacial lakes with periodic water discharge at the terminus of the Gongba Glacier in the southeastern Tibetan Plateau by utilizing 144 Sentinel-1A SAR images collected between October of 2014 and November of 2020. We first generated the reference intensity image (RII) by averaging all the SAR images collected when the water in the glacial lakes was wholly discharged, then calculated the neighborhood-based ratio between RII and each SAR intensity image, and finally identified the boundaries of the glacial lakes by a ratio threshold determined statistically. The time series of areas of the glacial lakes were estimated in this way, and the dates for water recharging and discharging were accordingly determined. The testing results showed that the water of the two glacial lakes began to be recharged in April and reached their peak in August and then remained stable dynamically until they began to shrink in October and were discharged entirely in February of the following year. We observed the expansion process with annual growth rates of 3.19% and 12.63% for these two glacial lakes, respectively, and monitored a glacial lake outburst flood event in July 2018. The validation by comparing with the results derived from Sentinel-2A/B optical images indicates that the accuracy for identifying the boundaries of the glacial lakes with Sentinel-1A SAR images can reach up to 96.49%. Generally, this contribution demonstrates the reliability and precision of SAR images to provide regular updates for the dynamic monitoring of glacial lakes.

Beschreibung: The pixel-based semantic segmentation methods take pixels as recognitions units, and are restricted by the limited range of receptive fields, so they cannot carry richer and higher-level semantics. These reduce the accuracy of remote sensing (RS) semantic segmentation to a certain extent. Comparing with the pixel-based methods, the graph neural networks (GNNs) usually use objects as input nodes, so they not only have relatively small computational complexity, but also can carry richer semantic information. However, the traditional GNNs are more rely on the context information of the individual samples and lack geographic prior knowledge that reflects the overall situation of the research area. Therefore, these methods may be disturbed by the confusion of “different objects with the same spectrum” or “violating the first law of geography” in some areas. To address the above problems, we propose a remote sensing semantic segmentation model called knowledge and spatial pyramid distance-based gated graph attention network (KSPGAT), which is based on prior knowledge, spatial pyramid distance and a graph attention network (GAT) with gating mechanism. The model first uses superpixels (geographical objects) to form the nodes of a graph neural network and then uses a novel spatial pyramid distance recognition algorithm to recognize the spatial relationships. Finally, based on the integration of feature similarity and the spatial relationships of geographic objects, a multi-source attention mechanism and gating mechanism are designed to control the process of node aggregation, as a result, the high-level semantics, spatial relationships and prior knowledge can be introduced into a remote sensing semantic segmentation network. The experimental results show that our model improves the overall accuracy by 4.43% compared with the U-Net Network, and 3.80% compared with the baseline GAT network.

Beschreibung: High-spatial-resolution land-surface temperature is required for several applications such as hydrological or climate studies. Global estimates of surface temperature are available from sensors observing in the infrared (IR), but without ‘all-weather’ observing capability. Passive microwave (MW) instruments can also be used to provide surface-temperature measurements but suffer from coarser spatial resolutions. To increase their resolution, a downscaling methodology applicable over different land environments and at any time of the day is proposed. The method uses a statistical relationship between clear sky-predicting variables and clear-sky temperatures to estimate temperature patterns that can be used in conjunction with coarse measurements to create high-resolution products. Different predicting variables are tested showing the need to use IR-derived information on vegetation, temperature diurnal evolution, and a temporal information. To build a true ‘all-weather’ methodology, the effect of clouds on surface temperatures is accounted for by correcting the clear-sky diurnal cycle amplitude, using cloud parameters from meteorological reanalysis. Testing the method on a coarse IR synthetic data at ∼25 km resolution yields a Root Mean Square Deviations (RMSD) between the ∼5 km high-resolution and downscaled temperatures smaller than 1 ∘C. When applied to observations by the Special Sensor Microwave Imager Sounder (SSMIS) at ∼25 km resolution, the downscaling to ∼5 km yields a smaller RMSD compared to IR observations. These results demonstrate the relevance of the methodology to downscale MW land-surface temperature and its potential to spatially enhanced the current ‘all-weather’ satellite monitoring of surface temperatures.

Beschreibung: In Ethiopia land degradation through soil erosion is of major concern. Land degradation mainly results from heavy rainfall events and droughts and is associated with a loss of vegetation and a reduction in soil fertility. To counteract land degradation in Ethiopia, initiatives such as the Sustainable Land Management Programme (SLMP) have been implemented. As vegetation condition is a key indicator of land degradation, this study used satellite remote sensing spatiotemporal trend analysis to examine patterns of vegetation between 2002 and 2018 in degraded land areas and studied the associated climate-related and human-induced factors, potentially through interventions of the SLMP. Due to the heterogeneity of the landscapes of the highlands of the Ethiopian Plateau and the small spatial scale at which human-induced changes take place, this study explored the value of using 30 m resolution Landsat data as the basis for time series analysis. The analysis combined Landsat derived Normalised Difference Vegetation Index (NDVI) data with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) derived rainfall estimates and used Theil-Sen regression, Mann-Kendall trend test and LandTrendr to detect changes in NDVI, rainfall and rain-use efficiency. Ordinary Least Squares (OLS) regression analysis was used to relate changes in vegetation directly to SLMP infrastructure. The key findings of the study are a general trend shift from browning between 2002 and 2010 to greening between 2011 and 2018 along with an overall greening trend between 2002 and 2018. Significant improvements in vegetation condition due to human interventions were found only at a small scale, mainly on degraded hillside locations, along streams or in areas affected by gully erosion. Visual inspections (based on Google Earth) and OLS regression results provide evidence that these can partly be attributed to SLMP interventions. Even from the use of detailed Landsat time series analysis, this study underlines the challenge and limitations to remotely sensed detection of changes in vegetation condition caused by land management interventions aiming at countering land degradation.

Beschreibung: Intermittently closed and open lakes or Lagoons (ICOLLs) are characterised by entrance barriers that form or break down due to the action of wind, waves and currents until the ocean-lagoon exchange becomes discontinuous. Entrance closure raises a variety of management issues that are regulated by monitoring. In this paper, those issues are investigated, and an automated sensor solution is proposed. Based upon a static Lidar paired with an edge computing device. This solar-powered remote sensing device provides an efficient way to automatically survey the lagoon entrance and estimate the berm profile. Additionally, it estimates the dry notch location and its height, critical factors in the management of the lagoon entrances. Generated data provide valuable insights into landscape evolution and berm behaviour during natural and mechanical breach events.

Beschreibung: The age of Mars yardangs is significant in studying their development and the evolution of paleoclimate conditions. For planetary surface or landforms, a common method for dating is based on the frequency and size distribution of all the superposed craters after they are formed. However, there is usually a long duration for the yardangs’ formation, and they will alter the superposed craters, making it impossible to give a reliable dating result with the method. An indirect method by analyzing the ages of the superposed layered ejecta was devised in the research. First, the layered ejecta that are superposed on and not altered by the yardangs are identified and mapped. Then, the ages of the layered ejecta are derived according to the crater frequency and size distribution on them. These ages indicate that the yardangs ceased development by these times, and the ages are valuable for studying the evolution of the yardangs. This indirect dating method was applied to the areas of Martian yardangs in the Medusae Fossae Formation (MFF). The ages of the selected six layered ejecta range from ~0.50 Ga to ~1.5 Ga, indicating that the evolution of the corresponding yardangs had been ceased before these times. Analysis of more layered ejecta craters and superposed yardangs implies that yardangs in the MFF have a long history of development and some yardangs are still in active development.

Beschreibung: In the past few decades, target detection from remote sensing images gained from aircraft or satellites has become one of the hottest topics. However, the existing algorithms are still limited by the detection of small remote sensing targets. Benefiting from the great development of computing power, deep learning has also made great breakthroughs. Due to a large number of small targets and complexity of background, the task of remote sensing target detection is still a challenge. In this work, we establish a series of feature enhancement modules for the network based on YOLO (You Only Look Once) -V3 to improve the performance of feature extraction. Therefore, we term our proposed network as FE-YOLO. In addition, to realize fast detection, the original Darknet-53 was simplified. Experimental results on remote sensing datasets show that our proposed FE-YOLO performs better than other state-of-the-art target detection models.

Beschreibung: Object detection is a significant and challenging problem in the study of remote sensing. Since remote sensing images are typically captured with a bird’s-eye view, the aspect ratios of objects in the same category may obey a Gaussian distribution. Generally, existing object detection methods ignore exploring the distribution character of aspect ratios for improving performance in remote sensing tasks. In this paper, we propose a novel Self-Adaptive Aspect Ratio Anchor (SARA) to explicitly explore aspect ratio variations of objects in remote sensing images. To be concrete, our SARA can self-adaptively learn an appropriate aspect ratio for each category. In this way, we can only utilize a simple squared anchor (related to the strides of feature maps in Feature Pyramid Networks) to regress objects in various aspect ratios. Finally, we adopt an Oriented Box Decoder (OBD) to align the feature maps and encode the orientation information of oriented objects. Our method achieves a promising mAP value of 79.91% on the DOTA dataset.

Beschreibung: Automated damage evaluation is of great importance in the maintenance and preservation of heritage structures. Damage investigation of large cultural buildings is time-consuming and labor-intensive, meaning that many buildings are not repaired in a timely manner. Additionally, some buildings in harsh environments are impossible to reach, increasing the difficulty of damage investigation. Oblique images facilitate damage detection in large buildings, yet quantitative damage information, such as area or volume, is difficult to generate. In this paper, we propose a method for quantitative damage evaluation of large heritage buildings in wild areas with repetitive structures based on drone images. Unlike existing methods that focus on building surfaces, we study the damage of building components and extract hidden linear symmetry information, which is useful for localizing missing parts in architectural restoration. First, we reconstruct a 3D mesh model based on the photogrammetric method using high-resolution oblique images captured by drone. Second, we extract 3D objects by applying advanced deep learning methods to the images and projecting the 2D object segmentation results to 3D mesh models. For accurate 2D object extraction, we propose an edge-enhanced method to improve the segmentation accuracy of object edges. 3D object fragments from multiple views are integrated to build complete individual objects according to the geometric features. Third, the damage condition of objects is estimated in 3D space by calculating the volume reduction. To obtain the damage condition of an entire building, we define the damage degree in three levels: no or slight damage, moderate damage and severe damage, and then collect statistics on the number of damaged objects at each level. Finally, through an analysis of the building structure, we extract the linear symmetry surface from the remaining damaged objects and use the symmetry surface to localize the positions of missing objects. This procedure was tested and validated in a case study (the Jiankou Great Wall in China). The experimental results show that in terms of segmentation accuracy, our method obtains results of 93.23% mAP and 84.21% mIoU on oblique images and 72.45% mIoU on the 3D mesh model. Moreover, the proposed method shows effectiveness in performing damage assessment of objects and missing part localization.

Beschreibung: A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a complexity science perspective by exploring the power law distribution of hotspot sizes and related socio-economic attributes. To detect hotspots, we rely on spatial clustering of geospatial big data sets, including street data from OpenStreetMap platform and nighttime light (NTL) data from the visible infrared imaging radiometer suite (VIIRS) imagery. Unlike conventional spatial units, which are imposed by governments or authorities (such as census block), the delineation of hotspots is done in a totally bottom-up manner and, more importantly, can help us examine precisely the scaling pattern of urban morphological and functional aspects. This results in two types of urban hotspots—street-based and NTL-based hotspots—being generated across 20 major cities in China. We find that Zipf’s law of hotspot sizes (both types) holds remarkably well for each city, as do the city-size distributions at the country level, indicating a statistically self-similar structure of geographic space. We further find that the urban scaling law can be effectively detected when using NTL-based hotspots as basic units. Furthermore, the comparison between two types of hotspots enables us to gain in-depth insights of urban planning and urban economic development.

Beschreibung: Megaproject social responsibility (MSR) is a critical factor regarding the sustainable delivery of megaprojects and MSR behavior to implement MSR. However, the questions of which factors effectively promote MSR behavior and how these factors work remain underexamined. Thus, this study aims to examine how the external institutional pressure and internal factors composed of relational behavior and pure altruistic values affect MSR behavior. On the basis of a conditional process analysis conducted on a set of survey data from various organizations involved in megaprojects in China, the results revealed that the higher the degree of mimetic pressure, the higher the degree of MSR behavior. Concurrently, relational behavior mediates the relationship between normative pressures and MSR behavior. Relational behavior also drives MSR behavior, and pure altruistic values moderate the relationships between institutional pressures and MSR behavior. These findings also provide practical suggestions for policymakers on the implementation and governance of MSR.

Beschreibung: Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively.

Beschreibung: In this paper, land subsidence susceptibility was assessed for Shahryar County in Iran using the adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. Another aim of the present paper was to assess if ensembles of ANFIS with two meta-heuristic algorithms (imperialist competitive algorithm (ICA) and gray wolf optimization (GWO)) would yield a better prediction performance. A remote sensing synthetic aperture radar (SAR) dataset from 2019 to 2020 and the persistent-scatterer SAR interferometry (PS-InSAR) technique were used to obtain a land subsidence inventory of the study area and use it for training and testing models. Resulting PS points were divided into two parts of 70% and 30% for training and testing the models, respectively. For susceptibility analysis, eleven conditioning factors were taken into account: the altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance to stream, distance to road, stream density, groundwater drawdown, and land use/land cover (LULC). A frequency ratio (FR) was applied to assess the correlation of factors to subsidence occurrence. The prediction power of the models and their generated land subsidence susceptibility maps (LSSMs) were validated using the root mean square error (RMSE) value and area under curve of receiver operating characteristic (AUC-ROC) analysis. The ROC results showed that ANFIS-ICA had the best accuracy (0.932) among the models (ANFIS-GWO (0.926), ANFIS (0.908)). The results of this work showed that optimizing ANFIS with meta-heuristics considerably improves LSSM accuracy although ANFIS alone had an acceptable result.

Beschreibung: The Sporadic-E (Es) layer is an often-observed phenomenon at high latitudes; however, our understanding of the polar cap Es layer is severely limited due to the scarce number of measurements. Here, the first comprehensive study of the polar cap Es layer associated with Global Positioning System (GPS) Total Electron Content (TEC) variations and scintillations is presented with multiple measurements at Resolute, Canada (Canadian Advanced Digital Ionosonde (CADI), Northward-looking face of Resolute Incoherent-Scatter Radar (RISR-N), and GPS receiver). According to the joint observations, the polar cap Es layer is a thin patch structure with variously high electron density, which gradually develops into the lower E region (~100 km) and horizontally extends 〉200 km. Moreover, the TEC variations produced by the polar cap Es layer are pulse-like enhancements with a general amplitude of ~0.5 TECu and are followed by smaller but rapid TEC perturbations. Furthermore, the possible scintillation effects likely associated with the polar cap Es layer are also discussed. As a consequence, the results widely expand our understanding on the polar cap Es layer, in particular on TEC variations.

Beschreibung: In both the Geospatial (Geo) and Building Information Modelling (BIM) domains, it is widely acknowledged that the integration of geo-data and BIM-data is beneficial and a crucial step towards solving the multi-disciplinary challenges of our built environment. The result of this integration—broadly termed GeoBIM—has the potential to be particularly beneficial in the context of the construction of large infrastructure projects, which could make use of data relating to the larger spatial extents typically handled in geographical information systems (GIS) as well as the detailed models generated by BIM. To date, GeoBIM integration has mainly been explored for buildings, in a 3D context and for small projects. This paper demonstrates the results of the next level of integration, exploring the addition of the fourth dimension by linking project schedule information to create 4D GeoBIM, examining interoperability challenges and benefits in the context of a number of use cases relating to the enabling works for a major commercial infrastructure project. The integrating power of location and time—knowing where and when data relate to—allows us to explore data interoperability challenges relating to linking real world construction data, created using commercial software, with other data sources; we are then able to demonstrate the benefits of 4D GeoBIM in the context of three decision making scenarios: examining the potential for prioritisation of noise mitigation interventions by identifying apartments closest to the noisiest construction process; development of a 4D location-enabled risk register allowing, for example, work to continue underground if a risk is specific to the top of a building; ensuring construction safety by using 3D buffering to ensure that the required distances between moving construction equipment and surrounding infrastructure are not breached. Additionally, once integrated, we are able to ‘democratize’ the data—make it accessible beyond the BIM and GIS expert group—by embedding it into a 3D/4D open source Web GIS tool.

Beschreibung: Soil loss is one of the most important causes of land degradation. It is an inevitable environmental and socio-economic problem that exists in many physiographic regions of the world, which, besides other impacts, has a direct bearing on agricultural productivity. A reliable estimate of soil loss is critical for designing and implementing any mitigation measures. We applied the widely used Revised Universal Soil Loss Equation (RUSLE) in the Khabur River Basin (KhRB) within the NW part of the Zagros Fold and Thrust Belt (ZFTB). The areas such as the NW Zagros range, characterized by rugged topography, steep slope, high rainfall, and sparse vegetation, are most susceptible to soil erosion. We used the Digital Elevation Model (DEM) of the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), and Landsat imagery to estimate annual soil loss using the RUSLE model. In addition, we estimated sediment yield (SY) at sub-basin scale, in the KhRB where a number of dams are planned, and where basic studies on soil erosion are lacking. Estimation of SY will be useful in mitigation of excessive sedimentation affecting dam performance and watershed management in this region. We determined the average annual soil loss and the SY in the KhRB to be 11.16 t.ha−1.y−1 and 57.79 t.ha−1.y−1, respectively. The rainfall and runoff erosivity (R factor), slope length (L factor), and slope steepness (S factor), are the three main factors controlling soil loss in the region. This is the first study to determine soil loss at the sub-basin scale along with identifying suitable locations for check dams to trap the sediment before it enters downstream reservoirs. The study provides valuable input data for design of the dams to prevent excessive siltation. This study also aims at offering a new approach in relating potential soil erosion to the actual erosion and hypsometric integrals.

Beschreibung: Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

Beschreibung: With continuous improvement of earth observation technology, source, and volume of remote sensing data are gradually enriched. It is critical to realize unified organization and to form data sharing service capabilities for massive remote sensing data effectively. We design a hierarchical multi-dimensional hybrid indexing model (HMDH), to address the problems in underlying organization and management, and improve query efficiency. Firstly, we establish remote sensing data grid as the smallest unit carrying and processing spatio-temporal information. We implement the construction of the HMDH in two steps, data classification based on fuzzy clustering algorithm, and classification optimization based on recursive neighborhood search algorithm. Then, we construct a hierarchical “cube” structure, filled with continuous space filling curves, to complete the coding of the HMDH. The HMDH reduces the amount of data to 6–17% and improves the accuracy to more than eight times than traditional grid model. Moreover, it can reduce the query time to 25% in some query scenarios than algorithms selected as the baseline in this paper. The HMDH model proposed can be used to solve the efficiency problems of fast and joint retrieval of remote sensing data. It extends the pattens of data sharing service and has a high application value.

Beschreibung: The chemical composition dataset of Aerosol Reanalysis of NASA’s Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRAero) has not been thoroughly evaluated with observation data in mainland China due to the lack of long-term chemical components data. Using the 5-year data of PM10 mass concentrations and chemical compositions obtained from the routine sampling measurements at the World Meteorological Organization the Global Atmosphere Watch Programme regional background stations, Jing Sha (JS) and Lin’An (LA), in central and eastern China, we comprehensively evaluate the surface PM10 concentrations and chemical compositions such as sulfate (SO42−), organic carbon (OC) and black carbon (BC) derived from MERRAero. Overall, the concentrations of PM10, SO42−, OC and BC from the MERRAero agreed well with the measurements, despite a slight and consistent overestimation of BC concentrations and a moderate and persistent underestimation of PM10 concentrations throughout the study period. The MERRAero reanalysis of aerosol compositions performs better during the summertime than wintertime. By considering the nitrate particles in PM10 reconstruction, MERRAero performance can be significantly improved. The unreasonable seasonal variations of PM10 chemical compositions at station LA by MERRAero could be causative factors for the larger MERRAero discrepancies during 2016–2017 than the period of 2011–2013.

Beschreibung: Construction is a hazardous industry. The project-based nature and fragmentation in the industry lead to change and uncertainty requiring special expertise. To handle those, construction firms must develop strategies and action plans along with the experience gained from lessons learned. Among the risks, safety risks are of critical importance leading to accidents. Hence, firms need to strengthen their safety programs, review their strategies for safety management, and develop effective safety training sessions to protect their workers. This study focuses on the success factors promoting safety performance. In this respect, a questionnaire was designed and administered to the Engineering News-Record (ENR) 2020 Top 400 Contractors. The questionnaire data was utilized in conducting a factor analysis to group and name the factors considering the total variance. The analysis of the factors resulted in six-factor groups; namely, project and firm-related factors, demographic factors, practical factors, motivational factors, organizational factors, and human-related factors. Project and firm-related factors were found to be the most essential factor group in terms of promoting the effectiveness of safety training. The results of this study are expected to guide industry practitioners in terms of reviewing and revising their safety training programs.

Beschreibung: To seek stochastic analogies in key processes related to the hydrological cycle, an extended collection of several billions of records from hundred thousands of worldwide stations is used in this work. The examined processes are the near-surface hourly temperature, dew point, relative humidity, sea level pressure, and atmospheric wind speed, as well as the hourly/daily streamflow and precipitation. Through the use of robust stochastic metrics such as the K-moments and a second-order climacogram (i.e., variance of the averaged process vs. scale), it is found that several stochastic similarities exist in both the marginal structure, in terms of the first four moments, and in the second-order dependence structure. Stochastic similarities are also detected among the examined processes, forming a specific hierarchy among their marginal and dependence structures, similar to the one in the hydrological cycle. Finally, similarities are also traced to the isotropic and nearly Gaussian turbulence, as analyzed through extensive lab recordings of grid turbulence and of turbulent buoyant jet along the axis, which resembles the turbulent shear and buoyant regime that dominates and drives the hydrological-cycle processes in the boundary layer. The results are found to be consistent with other studies in literature such as solar radiation, ocean waves, and evaporation, and they can be also justified by the principle of maximum entropy. Therefore, they allow for the development of a universal stochastic view of the hydrological-cycle under the Hurst–Kolmogorov dynamics, with marginal structures extending from nearly Gaussian to Pareto-type tail behavior, and with dependence structures exhibiting roughness (fractal) behavior at small scales, long-term persistence at large scales, and a transient behavior at intermediate scales.

Beschreibung: Changes in the land use/cover alter the Earth system processes and affect the provision of ecosystem services, posing a challenge to achieve sustainable development. In the past few decades, the Yellow River (YR) basin faced enormous social and environmental sustainability challenges associated with environmental degradation, soil erosion, vegetation restoration, and economic development, which makes it important to understand the long-term land use/cover dynamics of this region. Here, using three decades of Landsat imagery (17,080 images) and incorporating physiography data, we developed an effective annual land use/cover mapping framework and provided a set of 90 m resolution continuous annual land use/cover maps of the YR basin from 1986 to 2018 based on the Google Earth Engine and the Classification and Regression Trees algorithm. The independent random sampling validations based on the field surveys (640 points) and Google Earth (3456 points) indicated that the overall accuracy of these maps is 78.3% and 80.0%, respectively. The analysis of the land system of the YR basin showed that this region presents complex temporal and spatial changes, and the main change patterns include no change or little change, cropland loss and urban expansion, grassland restoration, increase in orchard and terrace, and increase in forest during the entire study period. The major land use/cover change has occurred in the transitions from forests, grasslands, and croplands to the class of orchard and terrace (19.8% of all change area), which not only increase the greenness but also raised the income, suggesting that YR progress towards sustainable development goals for livelihood security, economic growth, and ecological protection. Based on these data and analysis, we can further understand the role of the land system in the mutual feedback between society and the environment, and provide support for ecological conservation, high-quality development, and the formulation of sustainable management policies in this basin, highlighting the importance of continuous land use/cover information for understanding the interactions between the human and natural systems.

Beschreibung: Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.

Beschreibung: The area of dynamic planning of construction sites is unexplored. Although there is a large amount of scientific interest in the literature in dynamic planning of construction site layouts, with different methodologies developed, studies on construction site relocation do not exist. The purpose of this study is to cover this gap in the literature and contribute to the body of knowledge by presenting for the first time a dynamic planning of a construction site and its importance in linear construction projects and to validate this methodology through real case studies. The decisive variables that determine the appropriate site locations and the costs that arise from these choices are analyzed. The choice that maximizes the production rate of the construction site and thus minimizes the costs is further investigated. An algorithm has also been developed that estimates the cost of transportation of the equipment used in the project and thus enables the investigation of the “ideal” location that minimizes this cost. The “ideal” site location is examined according to the time schedule of the project at time intervals that are determined by the work progress. The optimization algorithm aims to minimize the cost that derives from non-productive activities. The validity of the proposed model is tested in four motorway projects. A sensitivity analysis concerning different sequences in the construction methods reveals remarkable changes in cost fluctuations depending on project size. The outcomes show that for the second, third, and fourth projects, dynamic planning is demanded, and the profit gained ranges from 1 to 1.5% of total budget cost. Financing expenses could be covered by this profit. The case studies presented are derived from linear infrastructure projects that are more sensitive to this approach because of their size and their budget that both affect the results.

Beschreibung: As cities are exposed to a portfolio of risks, the concept of resilience has risen to prominence over the past two decades. Consequently, a large volume of research has been published on different aspects of urban resilience. However, urban form resilience is still relatively understudied. As a step toward filling this gap, this study examines resilience of nine selected neighborhoods from Shiraz, an old Iranian city. The selected cases represent three different urban form patterns, namely, traditional, semi-planned, and planned. Different indicators related to the physical configuration of lots, blocks, open and green spaces, and street networks are used to examine resilience of each neighborhood to three major stressors, namely, earthquakes, extreme heat events, and floods. Additionally, a combination of Shannon entropy and the VIKOR (VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian) method is used to rank the resilience of each neighborhood to each of the three stressors. Results show that, overall, the physical form of the planned neighborhoods is more conducive to urban resilience. In contrast, the urban form of traditional neighborhoods was found to be less resilient. There were, however, some variations depending on the type of stressor considered. The paper concludes by emphasizing the need to consider social and economic factors in future studies of urban form resilience.

Beschreibung: The safety of the gas transmission infrastructure is one of the main concerns for infrastructure operating companies. Common gas pipelines’ tightness control is tedious and time-consuming. The development of new methods is highly desirable. This paper focuses on the applications of air-borne methods for inspections of the natural gas pipelines. The main goal of this study is to test an unmanned aerial vehicle (UAV), equipped with a remote sensing methane detector, for natural gas leak detection from the pipeline network. Many studies of the use of the UAV with laser detectors have been presented in the literature. These studies include experiments mainly on the artificial methane sources simulating gas leaks. This study concerns the experiments on a real leakage of natural gas from a pipeline. The vehicle at first monitored the artificial source of methane to determine conditions for further experiments. Then the experiments on the selected section of the natural gas pipelines were conducted. The measurement data, along with spatial coordinates, were collected and analyzed using machine learning methods. The analysis enabled the identification of groups of spatially correlated regions which have increased methane concentrations. Investigations on the flight altitude influence on the accuracy of measurements were also carried out. A range of between 4 m and 15 m was depicted as optimal for data collection in the natural gas pipeline inspections. However, the results from the field experiments showed that areas with increased methane concentrations are significantly more difficult to identify, though they are still noticeable. The experiments also indicate that the lower altitudes of the UAV flights should be chosen. The results showed that UAV monitoring can be used as a tool for the preliminary selection of potentially untight gas pipeline sections.

Beschreibung: We manually mapped particles ranging in longest axis from 0.3 cm to 95 m on (101955) Bennu for the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) asteroid sample return mission. This enabled the mission to identify candidate sample collection sites and shed light on the processes that have shaped the surface of this rubble-pile asteroid. Building on a global survey of particles, we used higher-resolution data from regional observations to calculate particle size-frequency distributions (PSFDs) and assess the viability of four candidate sites for sample collection (presence of unobstructed particles ≤ 2 cm). The four candidate sites have common characteristics: each is situated within a crater with a relative abundance of sampleable material. Their PSFDs, however, indicate that each site has experienced different geologic processing. The PSFD power-law slopes range from −3.0 ± 0.2 to −2.3 ± 0.1 across the four sites, based on images with a 0.01-m pixel scale. These values are consistent with, or shallower than, the global survey measurements. At one site, Osprey, the particle packing density appears to reach geometric saturation. We evaluate the uncertainty in these measurements and discuss their implications for other remotely sensed and mapped particles, and their importance to OSIRIS-REx sampling operations.

Beschreibung: Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

Beschreibung: Satellite video single object tracking has attracted wide attention. The development of remote sensing platforms for earth observation technologies makes it increasingly convenient to acquire high-resolution satellite videos, which greatly accelerates ground target tracking. However, overlarge images with small object size, high similarity among multiple moving targets, and poor distinguishability between the objects and the background make this task most challenging. To solve these problems, a deep Siamese network (DSN) incorporating an interframe difference centroid inertia motion (ID-CIM) model is proposed in this paper. In object tracking tasks, the DSN inherently includes a template branch and a search branch; it extracts the features from these two branches and employs a Siamese region proposal network to obtain the position of the target in the search branch. The ID-CIM mechanism was proposed to alleviate model drift. These two modules build the ID-DSN framework and mutually reinforce the final tracking results. In addition, we also adopted existing object detection datasets for remotely sensed images to generate training datasets suitable for satellite video single object tracking. Ablation experiments were performed on six high-resolution satellite videos acquired from the International Space Station and “Jilin-1” satellites. We compared the proposed ID-DSN results with other 11 state-of-the-art trackers, including different networks and backbones. The comparison results show that our ID-DSN obtained a precision criterion of 0.927 and a success criterion of 0.694 with a frames per second (FPS) value of 32.117 implemented on a single NVIDIA GTX1070Ti GPU.

Beschreibung: Intelligent transportation systems (ITS) provide safer, greener, and more convenient mobility, while reducing the impact on the environment. In recent years, simulation platforms have been employed to study ITS applications, mostly focusing on traffic-related simulations. Despite several research studies on ITS applications and simulation platforms, formal semantic descriptions of intelligent transportation systems have not been addressed yet. In this paper, a semantic model describing intelligent transportation systems for roads is proposed. The semantic model is devised to provide a basis for designing ITS simulation platforms. Building upon the semantic model, an extension to an open building information modeling (BIM) standard, i.e., the Industry Foundation Classes (IFC) schema, is presented. The IFC schema extension is verified and validated using a BIM-based simulation scenario of ITS for roads. It is shown that the proposed IFC-compliant description of ITS for roads provides a formal basis for generating BIM-based simulations and hence facilitates ITS infrastructure modeling in BIM projects. It is concluded that the present work represents a cornerstone for designing BIM-based ITS simulation platforms. In future endeavors, potential standardization and formalization efforts may be discussed.

Beschreibung: The Qaidam Basin is a unique and complex ecosystem, wherein elevation gradients lead to high spatial heterogeneity in vegetation dynamics and responses to environmental factors. Based on the remote sensing data of Moderate Resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission (TRMM) and Global Land Data Assimilation System (GLDAS), we analyzed the spatiotemporal variations of vegetation dynamics and responses to precipitation, accumulative temperature (AT) and soil moisture (SM) in the Qaidam Basin from 2001 to 2016. Moreover, the contribution of those factors to vegetation dynamics at different altitudes was analyzed via an artificial neural network (ANN) model. The results indicated that the Normalized Difference Vegetation Index (NDVI) values in the growing season showed an overall upward trend, with an increased rate of 0.001/year. The values of NDVI in low-altitude areas were higher than that in high-altitude areas, and the peak values of NDVI appeared along the elevation gradient at 4400–4600 m. Thanks to the use of ANN, we were able to detect the relative contribution of various environmental factors; the relative contribution rate of AT to the NDVI dynamic was the most significant (35.17%) in the low-elevation region (

Beschreibung: The Mediterranean Ridge accretionary complex (MAC) is a product of the convergence of Africa–Europe–Aegean plates. As a result, the region exhibits a continuous mass change (horizontal/vertical movements) that generates earthquakes. Over the last 50 years, approximately 430 earthquakes with M ≥ 5, including 36 M ≥ 6 earthquakes, have been recorded in the region. This study aims to link the ocean bottom deformations manifested through ocean bottom pressure variations with the earthquakes’ time series. To this end, we investigated the time series of the ocean bottom pressure (OBP) anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions. The OBP time series comprises a decreasing trend in addition to 1.02, 1.52, 4.27, and 10.66-year periodic components, which can be explained by atmosphere, oceans, and hydrosphere (AOH) processes, the Earth’s pole movement, solar activity, and core–mantle coupling. It can be inferred from the results that the OBP anomalies time series/mass change is linked to a rising trend and periods in the earthquakes’ energy time series. Based on this preliminary work, ocean-bottom pressure variation appears to be a promising lead for further research.

Beschreibung: A growing concern is given to the environmental impacts caused by the construction industry. Waste generation, resource consumption, and greenhouse gas emissions are the main drawbacks of the rapid urbanization that the world is witnessing. As a response to these pressing issues, policymakers and academia are exploring the concept of Circular Economy (CE) to manage resources better and achieve resource efficiency while eliminating waste. One of the strategies to implement CE in the built environment is to select the appropriate building materials and components from the early stages to carry out the concept’s principles along the value chain and create a closed-loop system. Therefore, this study aims at identifying selection criteria for building elements according to CE principles through a review of the latest research. Results have shown that little has been concretely achieved in terms of a paradigm shift to CE since the main focus of the literature is still the use of recycled products and the recyclability of building materials and components at their end-of-life. Although the present study is solely focused on the technical aspect of building materials and components, it outlines current adopted criteria to bring about a circular built environment and highlights the need for a more innovative approach to attain higher circularity levels.

Beschreibung: Large-scale ecosystem restoration projects (ERPs) have been implemented since the beginning of the new millennium to restore vegetation and improve the ecosystem in Southwest China. However, quantifying the effects of specific restoration activities, such as afforestation and grass planting, on vegetation recovery is difficult due to their incommensurable spatiotemporal distribution. Long-term and successive ERP-driven land use/cover changes (LUCCs) were used to recognise the spatiotemporal patterns of major restoration activities, and a contribution index was defined to assess the effects of these activities on gross primary production (GPP) dynamics in Southwest China during the period of 2001–2015. The results were as follows. (1) Afforestation and grass planting were major restoration activities that accounted for more than 54% of all LUCCs in Southwest China. Approximately 96% of restoration activities involved afforestation, and these activities were mostly distributed around Yunnan Province. (2) The Breathing Earth System Simulator (BESS) GPP performed better than the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP validated by field observation data. Nevertheless, their annual GPP trends were similar and increased by 12,581 g C m−2 d−1 and 13,406 g C m−2 d−1 for MODIS and BESS GPPs, respectively. (3) Although the afforestation and grass planting areas accounted for less than 1% of the total area of Southwest China, they contributed to more than 1% of the annual GPP increase in the entire study area. Afforestation directly contributed 14.94% (BESS GPP) or 24.64% (MODIS GPP) to the annual GPP increase. Meanwhile, grass planting directly contributed only 0.41% (BESS GPP) or 0.03% (MODIS GPP) to the annual GPP increase.

Beschreibung: Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio-temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented. New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.) and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet) will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales.

Beschreibung: A glacier surge, which is quasi-periodic and involves rapid flow, is an abnormal glacier motion. Although some glaciers have been found to be surging, little is known about surging glaciers on the Tibetan Plateau (TP), especially the Central and Northern TP. Here, we found a surging glacier (GLIMS ID: G085885E34389N) on the Zangser Kangri ice field (ZK), Central TP, by means of the digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM), TanDEM-X 90 m, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs, and High Mountain Asia 8-m DEM (HMA), combined with Landsat images and the Global Land Ice Velocity Extraction from Landsat 8 (GoLIVE) dataset. This surge event was confirmed by the crevasses, shear margin, and visible advancing snout shown in the Landsat images produced since 2014 and the HMA. The inter-comparison of these DEMs and the surface velocity changes showed that the surge event started between October 2012 and January 2014. The glacier may have also surged in the 1970s, based on a comparison between the topographical map and Landsat images. The glacier mass balance here has been slightly positive from 1999 onward (+0.03 ± 0.06 m w.e.a−1 from 1999 to 2015, +0.02 ± 0.07 m w.e.a−1 from 1999 to December 2011), which may indicate that the ZK is located on the southern edge of the mass balance anomaly on the TP. Combining with other surging glaciers on the Central and Northern TP, the relatively balanced mass condition, large size, and shallow slope can be associated with glacier surges on the Central and Northern TP.

Beschreibung: Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as the freely and open access policy adopted by the European Space Agency (ESA), software and tools are also available for data processing and deeper analysis. Nowadays, a bottleneck in this valuable chain is represented by the difficulty in shadow identification of Sentinel-2 data that, for precision agriculture applications, results in a tedious problem. To overcome the issue, we present a simplified tool, AgroShadow, to gain full advantage from Sentinel-2 products and solve the trade-off between omission errors of Sen2Cor (the algorithm used by the ESA) and commission errors of MAJA (the algorithm used by Centre National d’Etudes Spatiales/Deutsches Zentrum für Luft- und Raumfahrt, CNES/DLR). AgroShadow was tested and compared against Sen2Cor and MAJA in 33 Sentinel 2A-B scenes, covering the whole of 2020 and in 18 different scenarios of the whole Italian country at farming scale. AgroShadow returned the lowest error and the highest accuracy and F-score, while precision, recall, specificity, and false positive rates were always similar to the best scores which alternately were returned by Sen2Cor or MAJA.

Beschreibung: Very Long Baseline Interferometry (VLBI) solution can yield accurate information of angular position, and has been successfully used in the field of deep space exploration, such as astrophysics, imaging, detector positioning, and so on. The increase in VLBI data volume puts higher demands on efficient processing. Essentially, the main step of VLBI is the correlation processing, through which the angular position can be calculated. Since the VLBI correlation processing is both computation-intensive and data-intensive, the CPU cluster is usually employed in practical application to perform complex distributed computation. In this paper, we propose a parallel implementation of VLBI correlator based on graphics processing unit (GPU) to realize a more efficient and economical angular position calculation of deep space target. On the basis of massively GPU parallel computing, the coalesced access strategy and the parallel pipeline strategy are introduced to further accelerate the VLBI correlator. Experimental results show that the optimized GPU-based VLBI method can meet the real-time processing requirements of the received data stream. Compared with the sequential method, the proposed approach can reach a 224.1 × calculation speedup, and a 36.8 × application speedup. Compared with the multi-CPUs method, it can achieve 28.6 × calculation speedup and 4.7 × application speedup.

Beschreibung: Complex terrain features—in particular, environmental conditions, high population density and potential socio-economic damage—make the Trans-Mexican Volcanic Belt (TMVB) of particular interest regarding the study of deep convection and related severe weather. In this research, 10 years of Moderate-Resolution Imaging Spectroradiometer (MODIS) cloud observations are combined with Climate Hazards Group Infrared Precipitation with Station (CHIRPS) rainfall data to characterize the spatio-temporal distribution of deep convective clouds (DCCs) and their relationship to extreme precipitation. From monthly distributions, wet and dry phases are identified for cloud fraction, deep convective cloud frequency and convective precipitation. For both DCC and extreme precipitation events, the highest frequencies align just over the higher elevations of the TMVB. A clear relationship between DCCs and terrain features, indicating the important role of orography in the development of convective systems, is noticed. For three sub-regions, the observed distributions of deep convective cloud and extreme precipitation events are assessed in more detail. Each sub-region exhibits different local conditions, including terrain features, and are known to be influenced differently by emerging moisture fluxes from the Gulf of Mexico and the Pacific Ocean. The observed distinct spatio-temporal variabilities provide the first insights into the physical processes that control the convective development in the study area. A signal of the midsummer drought in Mexico (i.e., “canícula”) is recognized using MODIS monthly mean cloud observations.

Beschreibung: Precision agriculture relies on the rapid acquisition and analysis of agricultural information. An emerging method of agricultural monitoring is unmanned aerial vehicle low-altitude remote sensing (UAV-LARS), which possesses significant advantages of simple construction, strong mobility, and high spatial-temporal resolution with synchronously obtained image and spatial information. UAV-LARS could provide a high degree of overlap between X and Y during key crop growth periods that is currently lacking in satellite and remote sensing data. Simultaneously, UAV-LARS overcomes the limitations such as small scope of ground platform monitoring. Overall, UAV-LARS has demonstrated great potential as a tool for monitoring agriculture at fine- and regional-scales. Here, we systematically summarize the history and current application of UAV-LARS in Chinese agriculture. Specifically, we outline the technical characteristics and sensor payload of the available types of unmanned aerial vehicles and discuss their advantages and limitations. Finally, we provide suggestions for overcoming current limitations of UAV-LARS and directions for future work.

Beschreibung: The identification of urban functional areas is essential for urban planning and sustainable development. Spatial grids are the basic units for the implementation of urban plans and management by cities or development zones. The emergence of internet “big data” provides new ideas for the identification of urban functional areas. Based on point of interest (POI) data from Baidu Maps, the Xicheng District of Beijing was divided into grids with side lengths of 200, 500, and 1000 m in this study. The kernel density method was used to analyze the spatial structure of POI data. Two indicators, that is, the frequency density and category ratio, were then used to identify single- and mixed-functional areas. The results show that (1) commercial and financial areas are concentrated in the city center and multiple business centers have not developed; (2) scenic areas account for the largest proportion of single-functional areas in the Xicheng District of Beijing, followed by education and training, residence, and party and government organizations areas; and (3) the 200 × 200 m and 500 × 500 m grids are the most suitable for the identification of single- and mixed-functional areas, respectively.

Beschreibung: The purpose of this research is to illustrate, through a case study, a multilevel decision-making process able to support the decision maker in optimizing investment choices for the efficient allocation of public resources, with specific reference to recovery and adaptation to the reuse of unused historical public heritage. During the evaluation process, two methods in particular were combined according to a multiphase design: (i) a multi-criteria analysis to identify the Highest and Best Use among alternative reuse scenarios and (ii) a Discounted Cash Flow Analysis to support verification of the financial feasibility of the investment, in the hypothesis of an enhancement concession in a public–private partnership. Each method was applied in parallel to the evolution of the different design scenarios. The results of the study show that hybrid approaches are a promising line of research in the field of evaluation and urban design, applied to cultural heritage.

Beschreibung: Currently available high-resolution digital elevation model (DEM) is not particularly useful to geologists for understanding the long-term changes in fluvial landforms induced by tectonic uplift, although DEMs that are generated from satellite stereo images such as the ZiYuan-3 (ZY3) satellite include characteristics with significant coverage and rapid acquisition. Since an ongoing analysis of fluvial systems is lacking, the ZY3 DEM was generated from block adjustment to describe the mountainous area of the Qianhe Basin that have been induced by tectonic uplift. Moreover, we evaluated the overall elevation difference in ZY3 DEM, Shuttle Radar Topography Mission (1″ × 1″) (SRTM1), and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) by using the Ice Cloud and Land Elevation Satellite/Geoscience Laser Altimeter (ICESat/GLAH14) point cloud and a DEM of 1:50,000 scale. The values of the root mean square error (RMSE) of the elevation difference for ZY3 DEM were 9.31 and 9.71 m, respectively, and are in good agreement with SRTM1. The river long profiles and terrace heights were also extracted to compare the differences in channel steepness and the incision rates with SRTM1 and ASTER GDEM. Our results prove that ZY3 DEM would be a good alternative to SRTM1 in achieving the 1:50,000 scale for DEM products in China, while ASTER GDEM is unsuitable for extracting river longitudinal profiles. In addition, the northern and southern river incision rates were estimated using the ages and heights of river terraces, demonstrating a range from 0.12–0.45 to 0.10–0.33 m/kyr, respectively. Collectively, these findings suggest that ZY3 DEM is capable of estimating tectonic geomorphological features and has the potential for analyzing the continuous evolutionary response of a landscape to changes in climate and tectonics.

Beschreibung: A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

Beschreibung: Hydrological connectivity is an important characteristic of wetlands that maintains the stability and functions of an ecosystem. This study investigates the temporal variations of hydrological connectivity and their driving mechanism in Baiyangdian Lake, a large shallow wetland in North China, using a time series of open water surface area data derived from 36 Landsat 8 multispectral images from 2013–2019 and in situ measured water level data. Water area classification was implemented using the Google Earth Engine. Six commonly used indexes for extracting water surface data from satellite images were compared and the best performing index was selected for the water classification. A composite hydrological connectivity index computed from open water area data derived from Landsat 8 images was developed based on several landscape pattern indices and applied to Baiyangdian Lake. The results show that, reflectance in the near-infrared band is the most accurate index for water classification with 〉98% overall accuracy because of its sensitivity to different land cover types. The slopes of the best-fit linear relationships between the computed hydrological connectivity and observed water level show high variability between years. In most years, hydrological connectivity generally increases when water levels increase, with an average R2 of 0.88. The spatial distribution of emergent plants also varies year to year owing to interannual variations of the climate and hydrological regime. This presents a possible explanation for the variations in the annual relationship between hydrological connectivity and water level. For a given water level, the hydrological connectivity is generally higher in spring than summer and autumn. This can be explained by the fact that the drag force exerted by emergent plants, which reduces water flow, is smaller than that for summer and autumn owing to seasonal variations in the phenological characteristics of emergent plants. Our study reveals that both interannual and seasonal variations in the hydrological connectivity of Baiyangdian Lake are related to the growth of emergent plants, which occupy a large portion of the lake area. Proper vegetation management may therefore improve hydrological connectivity in this wetland.

Beschreibung: The belowground root zone of influence (ZOI) is fundamental to the study of the root–root and root–soil interaction mechanisms of plants and is vital for understanding changes in plant community compositions and ecosystem processes. However, traditional root research methods have a limited capacity to measure the actual ZOIs within plant communities without destroying them in the process. This study has developed a new approach to determining the ZOIs within natural plant communities. First, ground-penetrating radar (GPR), a non-invasive near-surface geophysical tool, was used to obtain a dataset of the actual spatial distribution of the coarse root system in a shrub quadrat. Second, the root dataset was automatically clustered and analyzed using the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to determine the ZOIs of different plants. Finally, the shape, size, and other characteristics of each ZOI were extracted based on the clustering results. The proposed method was validated using GPR-obtained root data collected in two field shrub plots and one simulation on a dataset from existing literature. The results show that the shrubs within the studied community exhibited either segregated and aggregated ZOIs, and the two types of ZOIs were distinctly in terms of shape and size, demonstrating the complexity of root growth in response to changes in the surrounding environment. The ZOIs extracted based on GPR survey data were highly consistent with the actual growth pattern of shrub roots and can thus be used to reveal the spatial competition strategies of plant roots responding to changes in the soil environment and the influence of neighboring plants.

Beschreibung: As a popular research direction in the field of intelligent transportation, road detection has been extensively concerned by many researchers. However, there are still some key issues in specific applications that need to be further improved, such as the feature processing of road images, the optimal choice of information extraction and detection methods, and the inevitable limitations of detection schemes. In the existing research work, most of the image segmentation algorithms applied to road detection are sensitive to noise data and are prone to generate redundant information or over-segmentation, which makes the computation of segmentation process more complicated. In addition, the algorithm needs to overcome objective factors such as different road conditions and natural environments to ensure certain execution efficiency and segmentation accuracy. In order to improve these issues, we integrate the idea of shallow machine-learning model that clusters first and then classifies in this paper, and a hierarchical multifeature road image segmentation integration framework is proposed. The proposed model has been tested and evaluated on two sets of road datasets based on real scenes and compared with common detection methods, and its effectiveness and accuracy have been verified. Moreover, it demonstrates that the method opens up a new way to enhance the learning and detection capabilities of the model. Most importantly, it has certain potential for application in various practical fields such as intelligent transportation or assisted driving.

Beschreibung: In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification. We made an optimization model for geometry-aware discrimination dictionary learning in which the dictionary learning (GADDL) is generalized from Euclidian space to Riemannian manifolds, and dictionary atoms are composed of manifold data. An efficient optimization algorithm based on an alternating direction multiplier method was developed to solve the model. Experiments were implemented on three public datasets: Flevoland-1989, San Francisco and Flevoland-1991. The experimental results show that the proposed method learned a discriminative dictionary with accuracies better those of comparative methods. The convergence of the model and the robustness of the initial dictionary were also verified through experiments.

Beschreibung: Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

Beschreibung: In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

Beschreibung: Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

Beschreibung: Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This paper presents such a model in which three conceptual levels of abstraction are proposed to frame an agent-based representation of movement decision-making processes: ‘attribute,’ ‘actor,’ and ‘autonomous agent’. These in combination with three temporal, spatial, and spatiotemporal general forms of observations distinguish nine (3 × 3) representation typologies of movement data within the agent framework. Thirdly, there are three levels of cognitive reasoning: ‘association,’ ‘intervention,’ and ‘counterfactual’. This makes for 27 possible types of operation embedded in a conceptual cube with the level of abstraction, type of observation, and degree of cognitive reasoning forming the three axes. The conceptual model is an arena where movement queries and the statement of relevant objectives takes place. An example implementation of a tightly constrained spatiotemporal scenario to ground the agent-structure was summarised. The platform has been well-defined so as to accommodate different tools and techniques to drive causal inference in computational movement analysis as an immediate future step.

Beschreibung: The incorporation of advanced technologies into Unmanned Aerial Vehicles (UAVs) platforms have enabled many practical applications in Precision Agriculture (PA) over the past decade. These PA tools offer capabilities that increase agricultural productivity and inputs’ efficiency and minimize operational costs simultaneously. However, these platforms also have some constraints that limit the application of UAVs in agricultural operations. The constraints include limitations in providing imagery of adequate spatial and temporal resolutions, dependency on weather conditions, and geometric and radiometric correction requirements. In this paper, a practical guide on technical characterizations of common types of UAVs used in PA is presented. This paper helps select the most suitable UAVs and on-board sensors for different agricultural operations by considering all the possible constraints. Over a hundred research studies were reviewed on UAVs applications in PA and practical challenges in monitoring and mapping field crops. We concluded by providing suggestions and future directions to overcome challenges in optimizing operational proficiency.

Beschreibung: This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p 〈 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P 〈 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P 〈 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

Beschreibung: In the Qihe area, the magnetic anomalies caused by deep and concealed magnetite are weak and compared with ground surveys, airborne surveys further weaken the signals. Moreover, the magnetite in the Qihe area belongs to a contact-metasomatic deposit, and the magnetic anomalies caused by the magnetite and its mother rock overlap and interweave. Therefore, it is difficult to directly delineate the target areas of magnetite according to the measured aeromagnetic maps in Qihe or similar areas, let alone estimate prospective magnetite resources. This study tried to extract magnetite-caused anomalies from aeromagnetic data by using high-pass filtering. Then, a preliminary estimation of magnetite prospective resources was realized by the 3D inversion of the extracted anomalies. In order to improve the resolution and accuracy of the inversion results, a combined model-weighting function was proposed for the inversion. Meanwhile, the upper and lower bounds and positive and negative constraints were imposed on the model parameters to further improve the rationality of the inversion results. A theoretical model with deep and concealed magnetite was established. It demonstrated the feasibility of magnetite-caused anomaly extraction and magnetite prospective resource estimation. Finally, the magnetite-caused anomalies were extracted from the measured aeromagnetic data and were consistent with known drilling information. The distribution of underground magnetic bodies was obtained by the 3D inversion of extracted anomalies, and the existing drilling data were used to delineate the volume of magnetite. In this way, the prospective resources of magnetite in Qihe area were estimated.

Beschreibung: Land use/land cover (LULC) change has been recognized as one of the most important indicators to study ecological and environmental changes. Remote sensing provides an effective way to map and monitor LULC change in real time and for large areas. However, with the increasing spatial resolution of remote sensing imagery, traditional classification approaches cannot fully represent the spectral and spatial information from objects and thus have limitations in classification results, such as the “salt and pepper” effect. Nowadays, the deep semantic segmentation methods have shown great potential to solve this challenge. In this study, we developed an adaptive band attention (BA) deep learning model based on U-Net to classify the LULC in the Three Gorges Reservoir Area (TGRA) combining RapidEye imagery and topographic information. The BA module adaptively weighted input bands in convolution layers to address the different importance of the bands. By comparing the performance of our model with two typical traditional pixel-based methods including classification and regression tree (CART) and random forest (RF), we found a higher overall accuracy (OA) and a higher Intersection over Union (IoU) for all classification categories using our model. The OA and mean IoU of our model were 0.77 and 0.60, respectively, with the BA module and were 0.75 and 0.58, respectively, without the BA module. The OA and mean IoU of CART and RF were both below 0.51 and 0.30, respectively, although RF slightly outperformed CART. Our model also showed a reasonable classification accuracy in independent areas well outside the training area, which indicates the strong model generalizability in the spatial domain. This study demonstrates the novelty of our proposed model for large-scale LULC mapping using high-resolution remote sensing data, which well overcomes the limitations of traditional classification approaches and suggests the consideration of band weighting in convolution layers.

Beschreibung: Riparian zones play an important role in the ecological stability of rivers. In particular, the quality of the riparian vegetation is a significant component of the hydromorphological status. In Europe, the QBR index (Qualitat del Bosc de Ribera) and the River Habitat Survey (RHS) are commonly used for the qualitative assessment of the riparian vegetation. In this study, we estimated the QBR index and the Riparian Quality index, which is derived from the RHS method, for 123 river reaches of the National Monitoring Network of Greece. Our field work included the completion of RHS and QBR protocols, as well as the use of Unmanned Aerial Vehicles (UAVs). The aim of this study is to assess the riparian vegetation status and to identify linkages with the dominant land uses within the catchment. Correlation analysis was used to identify the relationships between hydromorphological alterations and the degradation of the riparian vegetation, as well as their connection to land uses in the catchment area. Our results highlighted severe modifications of the riparian vegetation for the majority of the studied reaches. We also showed a differentiation of the QBR with respect to changes in the altitude and the land uses in the catchment area. Overall QBR reflects the variation in the riparian vegetation quality better than RQI. Our findings constitute an assessment of the status of the riparian zones in Greek rivers and set the basis for further research for the development of new and effective tools for a rapid quality assessment of the riparian zones.

Beschreibung: Streams and rivers of the Luquillo Experimental Forest, Puerto Rico, have been the subject of extensive watershed and aquatic research since the 1980s. This research includes understanding stream export of nutrients and coarse particulate organic matter, physicochemical constituents, aquatic fauna populations and community structure. However, many of the streams and watersheds studied do not appear in standard scale maps. We document recent collaborative and multi-institutional work to improve hydrological network information and identify knowledge gaps. The methods used to delimit and densify stream networks include establishment and incorporation of an updated new vertical datum for Puerto Rico, LIDAR derived elevation, and a combination of visual-manual and automated digitalization processes. The outcomes of this collaborative effort have resulted in improved watershed delineation, densification of hydrologic networks to reflect the scale of on-going studies, and the identification of constraining factors such as unmapped roadways, culverts, and other features of the built environment that interrupt water flow and alter runoff pathways. This work contributes to enhanced knowledge for watershed management, including attributes of riparian areas, effects of road and channel intersections and ridge to reef initiatives with broad application to other watersheds.

Beschreibung: Synthetic aperture radar (SAR) systems are susceptible to radio frequency interference (RFI). The existence of RFI will cause serious degradation of SAR image quality and a huge risk of target misjudgment, which makes the research on RFI suppression methods receive widespread attention. Since the location of the RFI source is one of the most vital information for achieving RFI spatial filtering, this paper presents a novel location method of multiple independent RFI sources based on direction-of-arrival (DOA) estimation and the non-convex optimization algorithm. It deploys an L-shaped multi-channel array on the SAR system to receive echo signals, and utilizes the two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm to estimate the positional relationship between the RFI source and the SAR system, ultimately combines the DOA estimation results of multiple azimuth time to calculate the geographic location of RFI sources through the particle swarm optimization (PSO) algorithm. Results on simulation experiments prove the effectiveness of the proposed method.

Beschreibung: One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures.

Beschreibung: Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.

Beschreibung: Water transparency, measured with Secchi disk depth (SDD), is an important parameter for describing the optical properties of a water body. This study evaluates variations of SDD and related impact factors in the Bohai and Yellow Seas (BYS). Based on a new mechanistic model proposed by Lee et al. (2015) applied to MODIS remote sensing reflectance data, climatological SDD variation from 2003 to 2019 was estimated. The annual mean images showed an increasing trend from the coastal zone to the deep ocean. Lower values were found in the Bohai Sea (BHS), while higher values observed in the center of the southern Yellow Sea (SYS). Additionally, the entire sea has shown a decreasing temporal tend, with the variation rate lowest in the BHS at 0.003 m y−1, and highest in the SYS at 0.015 m y−1. However, the weak increasing trend that appeared since 2017 suggests that water quality seems to have improved. Further, it displayed seasonal patterns of low in winter and spring and high in summer and autumn. The empirical orthogonal function (EOF) analysis of SDD variations over the BYS, shows that the first SDD EOF mode is the highest, strongly correlated with total suspended matter. With the high correlation coefficients of chromophoric dissolved organic matter, it illustrates that the SDD variation is mainly dominated by the optical components in the seawater, although correlation with chlorophyll-a is the weakest. The second and third EOF modes show that photosynthetically available radiation, sea surface temperature, sea surface salinity, and wind speed are the main covariates that cause SDD changes. Water transparency evaluation on a long-term scale is essential for water quality monitoring and marine ecosystem protection.

Beschreibung: In recent decades, multispectral and hyperspectral remote sensing data provide unprecedented opportunities for the initial stages of mineral exploration and environmental hazard monitoring [...]

Beschreibung: Polar Mesospheric Summer Echoes (PMSE) are distinct radar echoes from the Earth’s upper atmosphere between 80 to 90 km altitude that form in layers typically extending only a few km in altitude and often with a wavy structure. The structure is linked to the formation process, which at present is not yet fully understood. Image analysis of PMSE data can help carry out systematic studies to characterize PMSE during different ionospheric and atmospheric conditions. In this paper, we analyze PMSE observations recorded using the European Incoherent SCATter (EISCAT) Very High Frequency (VHF) radar. The collected data comprises of 18 observations from different days. In our analysis, the image data is divided into regions of a fixed size and grouped into three categories: PMSE, ionosphere, and noise. We use statistical features from the image regions and employ Linear Discriminant Analysis (LDA) for classification. Our results suggest that PMSE regions can be distinguished from ionosphere and noise with around 98 percent accuracy.

Beschreibung: The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted four Convolutional Neural Network (CNN) models for extracting damaged building information and compared their performance. A sample dataset of damaged buildings was constructed by using multiple disaster images retrieved from the xBD dataset. Using satellite and aerial remote sensing data obtained after the 2008 Wenchuan earthquake, we examined the geographic and data transferability of the deep network model pre-trained on the xBD dataset. The result shows that the network model pre-trained with samples generated from multiple disaster remote sensing images can extract accurately collapsed building information from satellite remote sensing data. Among the adjusted CNN models tested in the study, the adjusted DenseNet121 was the most robust. Transfer learning solved the problem of poor adaptability of the network model to remote sensing images acquired by different platforms and could identify disaster-damaged buildings properly. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.