The Experts below are selected from a list of 1533 Experts worldwide ranked by ideXlab platform
Peng Zhang - One of the best experts on this subject based on the ideXlab platform.
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quantifying Disaster physical Damage using remote sensing data a technical work flow and case study of the 2014 ludian earthquake in china
International Journal of Disaster Risk Science, 2017Co-Authors: Wei Wang, Ping Wang, Lingling Li, Peng ZhangAbstract:Disaster Damage assessment is an important basis for the objective assessment of the social impacts of Disasters and for the planning of recovery and reconstruction. It is also an important research field with regard to Disaster mitigation and risk management. Quantitative assessment of physical Damage refers to the determination of the physical Damage state of the exposed elements in a Disaster area, reflecting the aggregate quantities of Damages. It plays a key role in the comprehensive Damage assessment of major natural hazard-induced Disasters. The National Disaster Reduction Center of China has established a technical work flow for the quantitative assessment of Disaster physical Damage using remote sensing data. This article presents a quantitative assessment index system and method that can be integrated with high-resolution remote sensing data, basic geographical data, and field survey data. Following the 2014 Ludian Earthquake in Yunnan Province, China, this work flow was used to assess the Damage to buildings, roads, and agricultural and forest resources, and the assessment results were incorporated into the Disaster Damage Comprehensive Assessment Report of the 2014 Ludian Earthquake for the State Council of China. This article also outlines some possible improvements that can be addressed in future work.
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Quantifying Disaster Physical Damage Using Remote Sensing Data—A Technical Work Flow and Case Study of the 2014 Ludian Earthquake in China
International Journal of Disaster Risk Science, 2017Co-Authors: Wei Wang, Ping Wang, Lingling Li, Peng ZhangAbstract:Disaster Damage assessment is an important basis for the objective assessment of the social impacts of Disasters and for the planning of recovery and reconstruction. It is also an important research field with regard to Disaster mitigation and risk management. Quantitative assessment of physical Damage refers to the determination of the physical Damage state of the exposed elements in a Disaster area, reflecting the aggregate quantities of Damages. It plays a key role in the comprehensive Damage assessment of major natural hazard-induced Disasters. The National Disaster Reduction Center of China has established a technical work flow for the quantitative assessment of Disaster physical Damage using remote sensing data. This article presents a quantitative assessment index system and method that can be integrated with high-resolution remote sensing data, basic geographical data, and field survey data. Following the 2014 Ludian Earthquake in Yunnan Province, China, this work flow was used to assess the Damage to buildings, roads, and agricultural and forest resources, and the assessment results were incorporated into the Disaster Damage Comprehensive Assessment Report of the 2014 Ludian Earthquake for the State Council of China. This article also outlines some possible improvements that can be addressed in future work.
Wei Wang - One of the best experts on this subject based on the ideXlab platform.
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quantifying Disaster physical Damage using remote sensing data a technical work flow and case study of the 2014 ludian earthquake in china
International Journal of Disaster Risk Science, 2017Co-Authors: Wei Wang, Ping Wang, Lingling Li, Peng ZhangAbstract:Disaster Damage assessment is an important basis for the objective assessment of the social impacts of Disasters and for the planning of recovery and reconstruction. It is also an important research field with regard to Disaster mitigation and risk management. Quantitative assessment of physical Damage refers to the determination of the physical Damage state of the exposed elements in a Disaster area, reflecting the aggregate quantities of Damages. It plays a key role in the comprehensive Damage assessment of major natural hazard-induced Disasters. The National Disaster Reduction Center of China has established a technical work flow for the quantitative assessment of Disaster physical Damage using remote sensing data. This article presents a quantitative assessment index system and method that can be integrated with high-resolution remote sensing data, basic geographical data, and field survey data. Following the 2014 Ludian Earthquake in Yunnan Province, China, this work flow was used to assess the Damage to buildings, roads, and agricultural and forest resources, and the assessment results were incorporated into the Disaster Damage Comprehensive Assessment Report of the 2014 Ludian Earthquake for the State Council of China. This article also outlines some possible improvements that can be addressed in future work.
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Quantifying Disaster Physical Damage Using Remote Sensing Data—A Technical Work Flow and Case Study of the 2014 Ludian Earthquake in China
International Journal of Disaster Risk Science, 2017Co-Authors: Wei Wang, Ping Wang, Lingling Li, Peng ZhangAbstract:Disaster Damage assessment is an important basis for the objective assessment of the social impacts of Disasters and for the planning of recovery and reconstruction. It is also an important research field with regard to Disaster mitigation and risk management. Quantitative assessment of physical Damage refers to the determination of the physical Damage state of the exposed elements in a Disaster area, reflecting the aggregate quantities of Damages. It plays a key role in the comprehensive Damage assessment of major natural hazard-induced Disasters. The National Disaster Reduction Center of China has established a technical work flow for the quantitative assessment of Disaster physical Damage using remote sensing data. This article presents a quantitative assessment index system and method that can be integrated with high-resolution remote sensing data, basic geographical data, and field survey data. Following the 2014 Ludian Earthquake in Yunnan Province, China, this work flow was used to assess the Damage to buildings, roads, and agricultural and forest resources, and the assessment results were incorporated into the Disaster Damage Comprehensive Assessment Report of the 2014 Ludian Earthquake for the State Council of China. This article also outlines some possible improvements that can be addressed in future work.
N Kerle - One of the best experts on this subject based on the ideXlab platform.
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Disaster Damage detection through synergistic use of deep learning and 3d point cloud features derived from very high resolution oblique aerial images and multiple kernel learning
Isprs Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Anand Vetrivel, N Kerle, M Gerke, G VosselmanAbstract:Oblique aerial images offer views of both building roofs and facades, and thus have been recognized as a potential source to detect severe building Damages caused by destructive Disaster events such as earthquakes. Therefore, they represent an important source of information for first responders or other stakeholders involved in the post-Disaster response process. Several automated methods based on supervised learning have already been demonstrated for Damage detection using oblique airborne images. However, they often do not generalize well when data from new unseen sites need to be processed, hampering their practical use. Reasons for this limitation include image and scene characteristics, though the most prominent one relates to the image features being used for training the classifier. Recently features based on deep learning approaches, such as convolutional neural networks (CNNs), have been shown to be more effective than conventional hand-crafted features, and have become the state-of-the-art in many domains, including remote sensing. Moreover, often oblique images are captured with high block overlap, facilitating the generation of dense 3D point clouds – an ideal source to derive geometric characteristics. We hypothesized that the use of CNN features, either independently or in combination with 3D point cloud features, would yield improved performance in Damage detection. To this end we used CNN and 3D features, both independently and in combination, using images from manned and unmanned aerial platforms over several geographic locations that vary significantly in terms of image and scene characteristics. A multiple-kernel-learning framework, an effective way for integrating features from different modalities, was used for combining the two sets of features for classification. The results are encouraging: while CNN features produced an average classification accuracy of about 91%, the integration of 3D point cloud features led to an additional improvement of about 3% (i.e. an average classification accuracy of 94%). The significance of 3D point cloud features becomes more evident in the model transferability scenario (i.e., training and testing samples from different sites that vary slightly in the aforementioned characteristics), where the integration of CNN and 3D point cloud features significantly improved the model transferability accuracy up to a maximum of 7% compared with the accuracy achieved by CNN features alone. Overall, an average accuracy of 85% was achieved for the model transferability scenario across all experiments. Our main conclusion is that such an approach qualifies for practical use.
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towards automated satellite image segmentation and classification for assessing Disaster Damage using data specific features with incremental learning
6th International Conference on Geographic Object-Based Image Analysis 2016: Solutions & Synergies, 2016Co-Authors: Anand Vetrivel, N Kerle, M Gerke, G VosselmanAbstract:Automated Damage assessment based on satellite imagery is crucial for initiating fast response actions. Several methods based on supervised learning approaches have been reported as effective for automated mapping of Damages using remote sensing images. However, adopting these methods for practical use is still challenging, as they typically demand large amounts of training samples to build a supervised classifier, which are usually not readily available. With the advancement in technologies local and detailed Damage assessment for individual buildings is being made available, for example through analysis of images captured by unmanned aerial vehicles, monitoring systems installed in buildings, and through crowdsourcing. Often such assessments are being done in parallel, with results becoming available progressively. In this paper, an online classification strategy is adopted where a classifier is built incrementally using the streaming Damage labels from various sources as training samples, i.e. without retraining it from the scratch when new samples stream in. The Passive-Aggressive online classifier is used for the classification process. Apart from the classifier, the choice of image features plays a crucial role in the performance of the classification. The features extracted using recently reported deep learning approaches such as Convolutional Neural Networks (CNN), which learns features directly from images, have been reported to be more effective than conventional handcrafted features such as gray level co-occurrence matrix and Gabor wavelets. Thus in this study, the potential of CNN features is explored for online classification of satellite image to detect structural Damage, and is compared against handcrafted features. The feature extraction and classification process is carried out at an object level, where the objects are obtained by over-segmentation of the satellite image. The proposed online framework for Damage classification achieves a maximum overall accuracy of about 73%, which is comparable to that of batch classifier accuracy (74%) obtained for the same training and testing samples, however at a significantly lesser time and memory requirements. Moreover, the CNN features always significantly outperform handcrafted features.
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remote sensing based post Disaster Damage mapping with collaborative methods
Intelligente systems for crisis management : Geo-information for Disaster Managment 2012 Gi4DM, 2013Co-Authors: N KerleAbstract:Remote sensing has become an essential tool in post-Disaster response, including Damage assessment, traditionally done by professional analysts. However, geodata and—tools have become ubiquitous, which has allowed other organizations and laypersons to play a more prominent role in post Disaster response and assistance. This chapter addresses the prospects and challenges of collaborative Damage mapping. It discusses limitations and problems of traditional Damage mapping that may be overcome by modern Web 2.0-based methods. The response to the 2010 Haiti earthquake, in particular the collaborative Damage mapping by the GEO-CAN initiative and a number of problems associated with this activity, are discussed in detail. These center on the analysis problem inherent in image-based Damage assessment, but also the limited mutual understanding among the mapping organizers, map user and volunteer mappers. Cognitive task analysis (CTA) is recommended to address and overcome the cognitive challenges and demands in collaborative mapping.
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collaborative post Disaster Damage mapping via geo web services
Lecture Notes in Geoinformation and Cartography, 2010Co-Authors: Laban Maiyo, N Kerle, B KobbenAbstract:To mitigate the consequences of increasingly frequent Disasters across the globe, better real-time collaborative post-Disaster management tools are needed. The International Charter ‘Space and Major Disasters’, in conjunction with intermediary agencies, provides for space resources to be available to support Disaster response. It is widely seen as a successful example of international humanitarian assistance following Disasters. However, the Charter is also facing challenges with respect to lack of collaboration and validation, with the information flow being largely mono-directional. It is, therefore, fundamental to move away from static map data provision to a more dynamic, distributed and collaborative environment. Geo Web Services can bring together vast stores of data from heterogeneous sources, along with geospatial services that can interact in a loosely coupled environment and be used to create more suitable information for different stakeholders. The aim of this chapter is to evaluate the relevance and importance of Geo Web Services in the Disaster management domain and present a suitable Geo Web Service architecture for a collaborative post-Disaster Damage mapping system. We focus particularly on satellite image-based post-Disaster support situations, and present our ideas for a prototype based on this architecture with possibilities for User Generated Content.
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near real time post Disaster Damage assessment with airborne oblique video data
Geo-information for disaster management Gi4DM : proceedings of the 1st international symposium on geo-information for disaster management : Delft The , 2005Co-Authors: N Kerle, Rob Stekelenburg, Frank A Van Den Heuvel, B G H GorteAbstract:Natural and man-made Disasters lead to challenging situations for the affected communities, where comprehensive and reliable information on the nature, extent, and the consequences of an event are required. Providing timely information, however, is particularly difficult following sudden Disasters, such as those caused by earthquakes or industrial accidents. In those situations only partial, inaccurate or conflicting ground-based information is typically available, creating a well-recognized potential for satellite remote sensing to fill the gap. Despite continuous technical improvements, however, currently operational, non-classified, space-based sensors may not be able to provide timely data. In addition, even high spatial resolution satellites (< 1m) are limited in their capacity to reveal true 3D structural Damage at a level of detail necessary for appropriate Disaster response in urban areas.
Ping Wang - One of the best experts on this subject based on the ideXlab platform.
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quantifying Disaster physical Damage using remote sensing data a technical work flow and case study of the 2014 ludian earthquake in china
International Journal of Disaster Risk Science, 2017Co-Authors: Wei Wang, Ping Wang, Lingling Li, Peng ZhangAbstract:Disaster Damage assessment is an important basis for the objective assessment of the social impacts of Disasters and for the planning of recovery and reconstruction. It is also an important research field with regard to Disaster mitigation and risk management. Quantitative assessment of physical Damage refers to the determination of the physical Damage state of the exposed elements in a Disaster area, reflecting the aggregate quantities of Damages. It plays a key role in the comprehensive Damage assessment of major natural hazard-induced Disasters. The National Disaster Reduction Center of China has established a technical work flow for the quantitative assessment of Disaster physical Damage using remote sensing data. This article presents a quantitative assessment index system and method that can be integrated with high-resolution remote sensing data, basic geographical data, and field survey data. Following the 2014 Ludian Earthquake in Yunnan Province, China, this work flow was used to assess the Damage to buildings, roads, and agricultural and forest resources, and the assessment results were incorporated into the Disaster Damage Comprehensive Assessment Report of the 2014 Ludian Earthquake for the State Council of China. This article also outlines some possible improvements that can be addressed in future work.
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Quantifying Disaster Physical Damage Using Remote Sensing Data—A Technical Work Flow and Case Study of the 2014 Ludian Earthquake in China
International Journal of Disaster Risk Science, 2017Co-Authors: Wei Wang, Ping Wang, Lingling Li, Peng ZhangAbstract:Disaster Damage assessment is an important basis for the objective assessment of the social impacts of Disasters and for the planning of recovery and reconstruction. It is also an important research field with regard to Disaster mitigation and risk management. Quantitative assessment of physical Damage refers to the determination of the physical Damage state of the exposed elements in a Disaster area, reflecting the aggregate quantities of Damages. It plays a key role in the comprehensive Damage assessment of major natural hazard-induced Disasters. The National Disaster Reduction Center of China has established a technical work flow for the quantitative assessment of Disaster physical Damage using remote sensing data. This article presents a quantitative assessment index system and method that can be integrated with high-resolution remote sensing data, basic geographical data, and field survey data. Following the 2014 Ludian Earthquake in Yunnan Province, China, this work flow was used to assess the Damage to buildings, roads, and agricultural and forest resources, and the assessment results were incorporated into the Disaster Damage Comprehensive Assessment Report of the 2014 Ludian Earthquake for the State Council of China. This article also outlines some possible improvements that can be addressed in future work.
Lingling Li - One of the best experts on this subject based on the ideXlab platform.
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quantifying Disaster physical Damage using remote sensing data a technical work flow and case study of the 2014 ludian earthquake in china
International Journal of Disaster Risk Science, 2017Co-Authors: Wei Wang, Ping Wang, Lingling Li, Peng ZhangAbstract:Disaster Damage assessment is an important basis for the objective assessment of the social impacts of Disasters and for the planning of recovery and reconstruction. It is also an important research field with regard to Disaster mitigation and risk management. Quantitative assessment of physical Damage refers to the determination of the physical Damage state of the exposed elements in a Disaster area, reflecting the aggregate quantities of Damages. It plays a key role in the comprehensive Damage assessment of major natural hazard-induced Disasters. The National Disaster Reduction Center of China has established a technical work flow for the quantitative assessment of Disaster physical Damage using remote sensing data. This article presents a quantitative assessment index system and method that can be integrated with high-resolution remote sensing data, basic geographical data, and field survey data. Following the 2014 Ludian Earthquake in Yunnan Province, China, this work flow was used to assess the Damage to buildings, roads, and agricultural and forest resources, and the assessment results were incorporated into the Disaster Damage Comprehensive Assessment Report of the 2014 Ludian Earthquake for the State Council of China. This article also outlines some possible improvements that can be addressed in future work.
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Quantifying Disaster Physical Damage Using Remote Sensing Data—A Technical Work Flow and Case Study of the 2014 Ludian Earthquake in China
International Journal of Disaster Risk Science, 2017Co-Authors: Wei Wang, Ping Wang, Lingling Li, Peng ZhangAbstract:Disaster Damage assessment is an important basis for the objective assessment of the social impacts of Disasters and for the planning of recovery and reconstruction. It is also an important research field with regard to Disaster mitigation and risk management. Quantitative assessment of physical Damage refers to the determination of the physical Damage state of the exposed elements in a Disaster area, reflecting the aggregate quantities of Damages. It plays a key role in the comprehensive Damage assessment of major natural hazard-induced Disasters. The National Disaster Reduction Center of China has established a technical work flow for the quantitative assessment of Disaster physical Damage using remote sensing data. This article presents a quantitative assessment index system and method that can be integrated with high-resolution remote sensing data, basic geographical data, and field survey data. Following the 2014 Ludian Earthquake in Yunnan Province, China, this work flow was used to assess the Damage to buildings, roads, and agricultural and forest resources, and the assessment results were incorporated into the Disaster Damage Comprehensive Assessment Report of the 2014 Ludian Earthquake for the State Council of China. This article also outlines some possible improvements that can be addressed in future work.