Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan

Moya, Luis; Yamazaki, Fumio; Wen, Liang; Yamada, Masumi

doi:10.5194/nhess-18-65-2018

Cited by 46 publications

(46 citation statements)

References 23 publications

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“…An important constraint is the availability of a pre-event image. To the best of our experience, a temporal baseline from few months to about a year performs fairly well to identify damages in urban areas [12][13][14][15][16][17][18]. However, other types of land use may exhibit constant and systematic changes through time.…”

Section: Introductionmentioning

confidence: 94%

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

et al. 2019

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Synthetic aperture radar (SAR) images have been used to map flooded areas with great success. Flooded areas are often identified by detecting changes between a pair of images recorded before and after a certain flood. During the 2018 Western Japan Floods, the change detection method generated significant misclassifications for agricultural targets. To evaluate whether such a situation could be repeated in future events, this paper examines and identifies the causes of the misclassifications. We concluded that the errors occurred because of the following. (i) The use of only a single pair of SAR images from before and after the floods. (ii) The unawareness of the dynamics of the backscattering intensity through time in agricultural areas. (iii) The effect of the wavelength on agricultural targets. Furthermore, it is highly probable that such conditions might occur in future events. Our conclusions are supported by a field survey of 35 paddy fields located within the misclassified area and the analysis of Sentinel-1 time series data. In addition, in this paper, we propose a new parameter, which we named “conditional coherence”, that can be of help to overcome the referred issue. The new parameter is based on the physical mechanism of the backscattering on flooded and non-flooded agricultural targets. The performance of the conditional coherence as an input of discriminant functions to identify flooded and non-flooded agricultural targets is reported as well.

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Section: Introductionmentioning

confidence: 94%

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

et al. 2019

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“…However, in the near term, we suspect multi-rotor drones may be more difficult for urban overflight than fixedwing drones from a regulatory perspective. Most small multirotor drones have fixed-pitch rotors, which, in the event of power loss, do not allow for the drone to enter autorotation to substantially slow the descent (Perritt Jr. and Sprague, 2017). Fixed-wing drones, on the other hand, are able to glide after losing power (Perritt Jr. and Sprague, 2017).…”

Section: Key Lessons: Drone Mesh Resolution and Imaging Platformmentioning

confidence: 99%

Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada

Kucharczyk

Hugenholtz

2019

Nat. Hazards Earth Syst. Sci.

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Abstract. We report a case study using drone-based imagery to develop a pre-disaster 3-D map of downtown Victoria, British Columbia, Canada. This represents the first drone mapping mission over an urban area approved by Canada's aviation authority. The goal was to assess the quality of the pre-disaster 3-D data in the context of geospatial accuracy and building representation. The images were acquired with a senseFly eBee Plus fixed-wing drone with real-time kinematic/post-processed kinematic functionality. Results indicate that the spatial accuracies achieved with this drone would allow for sub-meter building collapse detection, but the non-gimbaled camera was insufficient for capturing building facades.

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“…Therefore, it is vital to monitor the status of buildings and provide high-precision building damage assessment after an earthquake at a detailed scale to support the emergency response and rescue activities. Considering the demand of the timely retrieval of disaster damage information, the implementation of satellite-based methods to assess damage in buildings has raised more and more attention after an earthquake, especially where the road connections are blocked or destroyed and access became thus difficult [1][2][3].…”

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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Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan

Cited by 46 publications

References 23 publications

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada

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

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