Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d Gis Model

Tu, Jihui; Sui, Haigang; Feng, Wenqing; Song, Ziteng

doi:10.5194/isprsannals-iii-8-43-2016

Cited by 16 publications

(9 citation statements)

References 3 publications

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“…They demonstrate that, although missing or smaller shadows compared to pre-event situation may indicate a damaged or collapsed buildings (e.g., pancake collapse), the sun direction differences between the acquired images resulting in shadow changes, can lead to confusion. Image texture has frequently been used as a proxy to identify damaged/collapsed buildings [54][55][56][57], damaged roads [58,59], and damaged areas [9,60] from RS imagery. Damaged buildings/areas have more irregular texture than intact ones [55]; therefore, the damaged/collapsed ones can be extracted by comparing their textures.…”

Section: Buildings Categorymentioning

confidence: 99%

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

2018

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Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods.While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery.2 of 30 threats, to reduce own overall vulnerability, and to allow the community to recover from adverse impacts when they occur. Decades of disaster research offer extensive findings in this respect [4][5][6][7]. Remote sensing (RS)-as an effective and rapid tool for monitoring large areas-is essential for the acquisition of geospatial data, which in turn constitutes the basis for risk assessment and management. RS is widely used for various aspects of the DRM, ranging from vulnerability [8] to rapid damage assessments [9], for diverse areas ranging from coastal ecosystems [10] to complex urban settings [11], and for disasters as diverse as landslides [12,13] or cyclones [14].Numerous methods have been developed to extract information from RS data to identify, characterize, or quantify different phases of the disaster risk cycle: response, recovery, prevention/mitigation, and preparedness [15]. However, early studies predominantly considered the physical side of the assessments for both ...

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Section: Buildings Categorymentioning

confidence: 99%

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

2018

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“…AD and QD can be calculated by Equations (8) and 9, respectively. The proportion of agreement C is estimated by Equation (10). The total disagreement D is the sum of AD and QD.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…It is also possible to integrate different methods to detect damaged buildings and produce accurate and reliable results. A novel method was proposed to detect damaged buildings using high-resolution remote sensing images and three-dimensional GIS data by Tu et al in Reference [10]. Remote sensing and GIS can be used to not only detect earthquake damage, but also to monitor the recovery after earthquakes, like how remote sensing and GIS were applied to monitor the recovery after the 2009 L'Aquila earthquake, Italy [11].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery

Liu

et al. 2019

Remote Sensing

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The accurate and quick derivation of the distribution of damaged building must be considered essential for the emergency response. With the success of deep learning, there is an increasing interest to apply it for earthquake-induced building damage mapping, and its performance has not been compared with conventional methods in detecting building damage after the earthquake. In the present study, the performance of grey-level co-occurrence matrix texture and convolutional neural network (CNN) features were comparatively evaluated with the random forest classifier. Pre- and post-event very high-resolution (VHR) remote sensing imagery were considered to identify collapsed buildings after the 2010 Haiti earthquake. Overall accuracy (OA), allocation disagreement (AD), quantity disagreement (QD), Kappa, user accuracy (UA), and producer accuracy (PA) were used as the evaluation metrics. The results showed that the CNN feature with random forest method had the best performance, achieving an OA of 87.6% and a total disagreement of 12.4%. CNNs have the potential to extract deep features for identifying collapsed buildings compared to the texture feature with random forest method by increasing Kappa from 61.7% to 69.5% and reducing the total disagreement from 16.6% to 14.1%. The accuracy for identifying buildings was improved by combining CNN features with random forest compared with the CNN approach. OA increased from 85.9% to 87.6%, and the total disagreement reduced from 14.1% to 12.4%. The results indicate that the learnt CNN features can outperform texture features for identifying collapsed buildings using VHR remotely sensed space imagery.

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“…Twodimensional (i.e., only using multi-perspective imagery) and 3-D (i.e., also with UAV-based 3DPCs) UAV data have been used for mono-temporal image-based SDA. Twodimensional image features have been studied for the recognition of damage patterns, among which texture features such as histogram of oriented gradients (HoG) and Gabor were found to be the most effective (Samadzadegan and Rastiveisi, 2008;Tu et al, 2016;Vetrivel et al, 2015). Vetrivel et al (2015) extracted Gabor wavelets and HoG texture descriptor features from UAV imagery for a supervised learning classification of damage-related structural gaps, by considering the distinctive damage textural pattern at these types of gaps' surroundings.…”

Section: Image-and Video-based Structural Damage Assessmentmentioning

confidence: 99%

Response RC1

Anonymous

2018

Preprint

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Remote sensing has evolved into the most efficient approach to assess post-disaster structural damage, in extensively affected areas through the use of spaceborne data. For smaller, and in particular, complex urban disaster scenes, multi-perspective aerial imagery obtained with unmanned aerial vehicles and derived dense color 3-D models are increasingly being used. These type of data allow the direct and automated recognition of damage-related features, supporting an effective post-disaster structural damage assessment. However, the rapid collection and sharing of multi-perspective aerial imagery is still limited due to tight or lacking regulations and legal frameworks. A potential alternative is aerial video footage, which is typically acquired and shared by civil protection institutions or news media and which tends to be the first type of airborne data available. Nevertheless, inherent artifacts and the lack of suitable processing means have long limited its potential use in structural damage assessment and other post-disaster activities. In this research the usability of modern aerial video data was evaluated based on a comparative quality and application analysis of video data and multi-perspective imagery (photos), and their derivative 3-D point clouds created using current photogrammetric techniques. Additionally, the effects of external factors, such as topography and the presence of smoke and moving objects, were determined by analyzing two different earthquake-affected sites: Tainan (Taiwan) and Pescara del Tronto (Italy). Results demonstrated similar usabilities for video and photos. This is shown by the short 2 cm of difference between the accuracies of video-and photobased 3-D point clouds. Despite the low video resolution, the usability of these data was compensated for by a small ground sampling distance. Instead of video characteristics, low quality and application resulted from non-data-related factors, such as changes in the scene, lack of texture, or moving objects. We conclude that not only are current video data more rapidly available than photos, but they also have a comparable ability to assist in image-based structural damage assessment and other post-disaster activities.

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Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d Gis Model

Cited by 16 publications

References 3 publications

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery

Response RC1

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