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  • 1
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    Seismic hazard and risk in Central Asia [Elektronische Ressource] (2011)
    Potsdam : Deutsches GeoForschungsZentrum
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    Call number: STR 11/14
    In: Scientific technical report
    Description / Table of Contents: This short report describes the first attempt at obtaining a preliminary cross-border risk model for Central Asia starting from datasets that were already available at the beginning of the EMCA Project.
    Pages: Online-Ressource
    Series Statement: Scientific technical report / Deutsches GeoForschungsZentrum GFZ 11/14
    URL: http://ebooks.gfz-potsdam.de/pubman/item/escidoc:43419
    Branch Library: GFZ Library
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  • 2
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    On-site early-warning system for Bishkek (Kyrgyzstan) (2015)
    Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Details
    Publication Date: 2015-04-25
    Description: In this work, the development of an on-site early warning system for Bishkek (Kyrgyzstan) is outlined. Several low cost sensors equipped with MEMS accelerometers are installed in eight buildings distributed within the urban area. The different sensing units communicate each other via wireless links and the seismic data are streamed in real-time to the data center using internet. Since each single sensing unit has computing capabilities, software for data processing can be installed to perform decentralized actions. In particular, each sensing unit can perform event detection task and run software for on-site early warning. If a description for the vulnerability of the building is uploaded in the sensing unit, this piece of information can be exploited to introduce the expected probability of damage in the early-warning protocol customized for a specific structure.
    Print ISSN: 1593-5213
    Topics: Geosciences
    Published by Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 3
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    Towards a cross-border exposure model for the Earthquake Model Central Asia (2015)
    Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Details
    Publication Date: 2015-04-25
    Description: This work provides an insight into the development of the first harmonized exposure model for Central Asia. The model was derived in the frame of the Earthquake Model Central Asia (EMCA) project, which is the regional initiative for Central Asia to the Global Earthquake Model (GEM). The EMCA exposure model combines commonly used data sources and acquisition techniques (e.g., rapid visual screening) with novel rapid assessment approaches (e.g., satellite remote sensing and omnidirectional imaging) in the framework of an integrated sampling scheme and stores the data in a multi-resolution spatio-temporal database. The exposure model implements a newly developed building typology, harmonized for the five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and follows the international building taxonomy of the GEM. Emphasis is given to the multi-scale nature and the temporal dynamics of exposure data. Results from a selected urban area are provided to illustrate the current state of the continuously updated exposure model.
    Print ISSN: 1593-5213
    Topics: Geosciences
    Published by Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 4
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    Focus maps: a means of prioritizing data collection for efficient geo-risk assessment (2015)
    Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Details
    Publication Date: 2015-04-25
    Description:   Efficient Disaster Risk Reduction (DRR) management constantly calls upon high-quality information to be collected or updated for vulnerability monitoring and risk assessment. This process is often resource- and time-intensive, which many economically developing states (including most Central Asian countries) can seldom afford. In this paper, we introduce the concept of focus maps as a useful tool to quantify the spatial probability of sampling. Focus maps allow for a data collection prioritization scheme to be put in place, enabling the realization of optimized spatial sampling which assigns a higher priority to locations where the need for high-quality information is greater. In practice, smaller samples can be drawn with the same (or better) resulting accuracy of the estimates, resulting in a more efficient use of time and resources. The factors that affect such a spatial sampling scheme include the usual components of risk assessment (hazard, exposure, vulnerability) where available, as well as other potentially critical factors, such as the extent and quality of previously collected data. The practical application of the proposed approach to the case of Central Asia will be exemplified and discussed.
    Print ISSN: 1593-5213
    Topics: Geosciences
    Published by Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 5
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    IJGI, Vol. 1, Pages 69-88: Exposure Estimation from Multi-Resolution Optical Satellite Imagery for Seismic Risk Assessment (2012)
    MDPI Publishing
    Details
    Publication Date: 2012-05-30
    Description: Given high urbanization rates and increasing spatio-temporal variability in many present-day cities, exposure information is often out-of-date, highly aggregated or spatially fragmented, increasing the uncertainties associated with seismic risk assessments. This work therefore aims at using space-based technologies to estimate, complement and extend exposure data at multiple scales, over large areas and at a comparatively low cost for the case of the city of Bishkek, Kyrgyzstan. At a neighborhood scale, an analysis of urban structures using medium-resolution optical satellite images is performed. Applying image classification and change-detection analysis to a time-series of Landsat images, the urban environment can be delineated into areas of relatively homogeneous urban structure types, which can provide a first estimate of an exposed building stock (e.g., approximate age of structures, composition and distribution of predominant building types). At a building-by-building scale, a more detailed analysis of the exposed building stock is carried out using a high-resolution Quickbird image. Furthermore, the multi-resolution datasets are combined with census data to disaggregate population statistics. The tools used within this study are being developed on a free- and open-source basis and aim at being transparent, usable and transferable.
    Electronic ISSN: 2220-9964
    Topics: Architecture, Civil Engineering, Surveying , Geosciences
    Published by MDPI Publishing on behalf of International Society for Photogrammetry and Remote Sensing (ISPRS).
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  • 6
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    Remote Sensing, Vol. 6, Pages 2912-2939: Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images (2014)
    MDPI Publishing
    Details
    Publication Date: 2014-04-01
    Description: In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS) and very high resolution (WorldView-2, Quickbird, Ikonos) multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based), have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.
    Electronic ISSN: 2072-4292
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by MDPI Publishing
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  • 7
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    Sensors, Vol. 17, Pages 2400: The Multi-Parameter Wireless Sensing System (MPwise): Its Description and Application to Earthquake Risk Mitigation (2017)
    MDPI Publishing
    In: Sensors
    Details
    Publication Date: 2017-10-21
    Description: Sensors, Vol. 17, Pages 2400: The Multi-Parameter Wireless Sensing System (MPwise): Its Description and Application to Earthquake Risk Mitigation Sensors doi: 10.3390/s17102400 Authors: Tobias Boxberger Kevin Fleming Massimiliano Pittore Stefano Parolai Marco Pilz Stefan Mikulla The Multi-Parameter Wireless Sensing (MPwise) system is an innovative instrumental design that allows different sensor types to be combined with relatively high-performance computing and communications components. These units, which incorporate off-the-shelf components, can undertake complex information integration and processing tasks at the individual unit or node level (when used in a network), allowing the establishment of networks that are linked by advanced, robust and rapid communications routing and network topologies. The system (and its predecessors) was originally designed for earthquake risk mitigation, including earthquake early warning (EEW), rapid response actions, structural health monitoring, and site-effect characterization. For EEW, MPwise units are capable of on-site, decentralized, independent analysis of the recorded ground motion and based on this, may issue an appropriate warning, either by the unit itself or transmitted throughout a network by dedicated alarming procedures. The multi-sensor capabilities of the system allow it to be instrumented with standard strong- and weak-motion sensors, broadband sensors, MEMS (namely accelerometers), cameras, temperature and humidity sensors, and GNSS receivers. In this work, the MPwise hardware, software and communications schema are described, as well as an overview of its possible applications. While focusing on earthquake risk mitigation actions, the aim in the future is to expand its capabilities towards a more multi-hazard and risk mitigation role. Overall, MPwise offers considerable flexibility and has great potential in contributing to natural hazard risk mitigation.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI Publishing
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  • 8
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    IJGI, Vol. 6, Pages 114: A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution (2017)
    MDPI Publishing
    Details
    Publication Date: 2017-04-09
    Description: With an ever-increasing volume and complexity of data collected from a variety of sources, the efficient management of geospatial information becomes a key topic in disaster risk management. For example, the representation of assets exposed to natural disasters is subjected to changes throughout the different phases of risk management reaching from pre-disaster mitigation to the response after an event and the long-term recovery of affected assets. Spatio-temporal changes need to be integrated into a sound conceptual and technological framework able to deal with data coming from different sources, at varying scales, and changing in space and time. Especially managing the information life-cycle, the integration of heterogeneous information and the distributed versioning and release of geospatial information are important topics that need to become essential parts of modern exposure modelling solutions. The main purpose of this study is to provide a conceptual and technological framework to tackle the requirements implied by disaster risk management for describing exposed assets in space and time. An information life-cycle management solution is proposed, based on a relational spatio-temporal database model coupled with Git and GeoGig repositories for distributed versioning. Two application scenarios focusing on the modelling of residential building stocks are presented to show the capabilities of the implemented solution. A prototype database model is shared on GitHub along with the necessary scenario data.
    Electronic ISSN: 2220-9964
    Topics: Architecture, Civil Engineering, Surveying , Geosciences
    Published by MDPI Publishing on behalf of International Society for Photogrammetry and Remote Sensing (ISPRS).
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  • 9
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    IJGI, Vol. 4, Pages 2401-2427: Improving Post-Earthquake Insurance Claim Management: A Novel Approach to Prioritize Geospatial Data Collection (2015)
    MDPI Publishing
    Details
    Publication Date: 2015-10-31
    Description: With a population exceeding 14 million and a GDP of more than 300 billion USD, Istanbul dominates the Turkish economy. Unfortunately, this concentration of social and economic assets is permanently threatened by potentially devastating earthquakes, given the city’s close proximity to several well-known fault systems. As a measure to mitigate the consequences of such events, and to increase the resilience of the exposed communities, the Turkish Catastrophe Insurance Pool (TCIP) has been set up to provide affordable and reliable earthquake insurance to households all over the country. In the aftermath of a damaging event, especially in Istanbul, the operational capacity of TCIP will be seriously challenged by the high number of claims whose settlement would have to be swift and fair in order to kick-start the recovery process. In this paper we explore an integrated approach based on mobile mapping and ad hoc prioritization techniques to streamline the data collection and analysis process, with application to both the pre-event and post-event phases. Preliminary results obtained in Besiktas, a populous district of Istanbul, are presented and discussed.
    Electronic ISSN: 2220-9964
    Topics: Architecture, Civil Engineering, Surveying , Geosciences
    Published by MDPI Publishing on behalf of International Society for Photogrammetry and Remote Sensing (ISPRS).
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  • 10
    Electronic Resource
    Electronic Resource
    Learning to Recognize Visual Dynamic Events from Examples (2000)
    Springer
    International journal of computer vision 38 (2000), S. 35-44 
    Details
    ISSN: 1573-1405
    Keywords: dynamic events ; pattern recognition ; Support Vector Machines ; computer vision systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes a trainable and flexible system able to recognize visual dynamic events, e.g. movements performed by different people, from a stream of images taken by a fixed camera. Each event is represented by a feature vector built from the spatio-temporal changes detected in the observed image sequence. The system neither attempts to recover the 3D structure nor assumes a prior model of the observed dynamic events. During training a supervisor identifies and labels the events of interest among those automatically detected by the system. At run time, previously unseen events are detected and classified on the basis of the available examples. Several experiments on real images are reported and the benefits of using Support Vector Machines for performing effective classification from a relatively small number of labeled examples and for building noise tolerant representations are discussed. Preliminary results indicate that the proposed system can also be applied with equally good results to the case in which the dynamic events are gestures performed by different people.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008114700759
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