Articles | Volume 18, issue 1
https://doi.org/10.5194/nhess-18-65-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-18-65-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Jan 2018
Research article |
| 04 Jan 2018
Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan
Luis Moya
CORRESPONDING AUTHOR
International Research Institute of Disaster Science, Tohoku
University, Miyagi, Sendai, 980-0845, Japan
Fumio Yamazaki
Department of Urban
Environment Systems, Chiba University, Chiba 263-8522, Japan
Wen Liu
Department of Urban
Environment Systems, Chiba University, Chiba 263-8522, Japan
Masumi Yamada
Disaster Prevention Research Institute, Kyoto University, Gokasho,
Uji, 611-0011, Japan
Viewed
Total article views: 5,020 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 19 Jun 2017)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,424 | 2,375 | 221 | 5,020 | 185 | 262 |
- HTML: 2,424
- PDF: 2,375
- XML: 221
- Total: 5,020
- BibTeX: 185
- EndNote: 262
Total article views: 4,270 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 Jan 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,076 | 1,989 | 205 | 4,270 | 173 | 249 |
- HTML: 2,076
- PDF: 1,989
- XML: 205
- Total: 4,270
- BibTeX: 173
- EndNote: 249
Total article views: 750 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 19 Jun 2017)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 348 | 386 | 16 | 750 | 12 | 13 |
- HTML: 348
- PDF: 386
- XML: 16
- Total: 750
- BibTeX: 12
- EndNote: 13
Viewed (geographical distribution)
Total article views: 5,020 (including HTML, PDF, and XML)
Thereof 4,773 with geography defined
and 247 with unknown origin.
Total article views: 4,270 (including HTML, PDF, and XML)
Thereof 4,038 with geography defined
and 232 with unknown origin.
Total article views: 750 (including HTML, PDF, and XML)
Thereof 735 with geography defined
and 15 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
39 citations as recorded by crossref.
- A Weakly Supervised Bitemporal Scene Change Detection Approach for Pixel-Level Building Damage Assessment Using Pre- and Post-Disaster High-Resolution Remote Sensing Images W. Qiao et al. https://doi.org/10.1109/TGRS.2024.3494257
- Big Data in Natural Disaster Management: A Review M. Yu et al. https://doi.org/10.3390/geosciences8050165
- Heritage Building Era Detection using CNN M. Samaun Hasan et al. https://doi.org/10.1088/1757-899X/617/1/012016
- Statistical analysis and modeling to examine the exterior and interior building damage pertaining to the 2016 Kumamoto earthquake H. Xiu et al. https://doi.org/10.1177/87552930211035408
- 3D gray level co-occurrence matrix and its application to identifying collapsed buildings L. Moya et al. https://doi.org/10.1016/j.isprsjprs.2019.01.008
- SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan M. Hajeb et al. https://doi.org/10.3390/app10248932
- Data collection tools for post-disaster damage assessment of building and lifeline infrastructure systems J. Lozano & I. Tien https://doi.org/10.1016/j.ijdrr.2023.103819
- Artificial intelligence-empowered point cloud technologies for rapid resilience assessment of urban infrastructure under environment extremes: A comprehensive review Y. Tang et al. https://doi.org/10.1016/j.engappai.2026.114086
- Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami L. Moya et al. https://doi.org/10.1016/j.rse.2020.111743
- A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery M. Ji et al. https://doi.org/10.3390/rs11101202
- Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images B. Kalantar et al. https://doi.org/10.3390/rs12213529
- DAMAGE ESTIMATION OF BUILDINGS’ ROOFS DUE TO THE 2019 TYPHOON FAXAI USING THE POST-EVENT AERIAL PHOTOGRAPHS W. LIU & Y. MARUYAMA https://doi.org/10.2208/jscejhe.76.1_166
- Tsunami Damage Detection with Remote Sensing: A Review S. Koshimura et al. https://doi.org/10.3390/geosciences10050177
- Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment S. Al Shafian & D. Hu https://doi.org/10.3390/buildings14082344
- Early estimation of ground displacements and building damage after seismic events using SAR and LiDAR data: The case of the Amatrice earthquake in central Italy, on 24th August 2016 L. Saganeiti et al. https://doi.org/10.1016/j.ijdrr.2020.101924
- Applications of artificial intelligence for disaster management W. Sun et al. https://doi.org/10.1007/s11069-020-04124-3
- Statistical analysis of earthquake debris extent from wood‐frame buildings and its use in road networks in Japan L. Moya et al. https://doi.org/10.1177/8755293019892423
- DS-Net: A dedicated approach for collapsed building detection from post-event airborne point clouds H. Xiu et al. https://doi.org/10.1016/j.jag.2022.103150
- Quantitative assessment of earthquake-induced building damage at regional scale using LiDAR data F. Foroughnia et al. https://doi.org/10.1016/j.ijdrr.2024.104403
- Applications of Artificial Intelligence and Future Research Directions for Radiation Emergency Response Y. Park et al. https://doi.org/10.7232/JKIIE.2022.48.6.626
- Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods L. Moya et al. https://doi.org/10.3390/rs11192320
- Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada M. Kucharczyk & C. Hugenholtz https://doi.org/10.5194/nhess-19-2039-2019
- Digitalization and Spatial Documentation of Post-Earthquake Temporary Housing in Central Italy: An Integrated Geomatic Approach Involving UAV and a GIS-Based System I. Tonti et al. https://doi.org/10.3390/drones7070438
- A rapid evaluation method of the seismic damage to buildings based on UAV images D. Jia et al. https://doi.org/10.1016/j.geomat.2024.100006
- Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence A. Portillo & L. Moya https://doi.org/10.3390/rs15112754
- DALi-CGAN: domain adaptive LiDAR-based conditional generative adversarial network for earthquake damage assessment P. Talreja & S. Durbha https://doi.org/10.1080/19475705.2026.2659167
- Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data Y. Nie et al. https://doi.org/10.3390/rs13061146
- Non-Contact damage detection of drainage wells during the Mj7.6 Noto Peninsula earthquake by UAV-LiDAR technique K. Shibano & T. Suzuki https://doi.org/10.1016/j.measurement.2025.117610
- Airborne LiDAR for post-disaster recovery in Japan – lessons from coseismic landslides and lifeline impacts in the aftermath of the 1 January 2024 Noto Peninsula earthquake C. Gomez https://doi.org/10.1144/gh2025-10
- Collapsed Building Detection Using 3D Point Clouds and Deep Learning H. Xiu et al. https://doi.org/10.3390/rs12244057
- Use of Multi-Temporal LiDAR Data to Extract Collapsed Buildings and to Monitor Their Removal Process after the 2016 Kumamoto Earthquake F. Yamazaki et al. https://doi.org/10.3390/rs14235970
- Building Damage Assessment Using Feature Concatenated Siamese Neural Network M. Ramadhan et al. https://doi.org/10.1109/ACCESS.2024.3361287
- New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images Y. Endo et al. https://doi.org/10.3390/rs10122059
- Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami Y. Bai et al. https://doi.org/10.3390/rs10101626
- Data Collection Using Terrestrial Laser Scanners from the Shake-Table Test of a Full-Scale Reinforced Concrete Building P. Calvi et al. https://doi.org/10.1061/JSENDH.STENG-12627
- DETECTION OF DAMAGED BUILDINGS BECAUSE OF AN EARTHQUAKE BASED ON DEEP LEARNING OF AERIAL LASER SURVEY DATA A. KAGOSHIMA et al. https://doi.org/10.2208/jscejj.22-13020
- LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems E. Kaartinen et al. https://doi.org/10.3390/s22124610
- Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data W. Liu et al. https://doi.org/10.3390/rs11192292
- Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification L. Moya et al. https://doi.org/10.1109/TGRS.2020.3046004
39 citations as recorded by crossref.
- A Weakly Supervised Bitemporal Scene Change Detection Approach for Pixel-Level Building Damage Assessment Using Pre- and Post-Disaster High-Resolution Remote Sensing Images W. Qiao et al. https://doi.org/10.1109/TGRS.2024.3494257
- Big Data in Natural Disaster Management: A Review M. Yu et al. https://doi.org/10.3390/geosciences8050165
- Heritage Building Era Detection using CNN M. Samaun Hasan et al. https://doi.org/10.1088/1757-899X/617/1/012016
- Statistical analysis and modeling to examine the exterior and interior building damage pertaining to the 2016 Kumamoto earthquake H. Xiu et al. https://doi.org/10.1177/87552930211035408
- 3D gray level co-occurrence matrix and its application to identifying collapsed buildings L. Moya et al. https://doi.org/10.1016/j.isprsjprs.2019.01.008
- SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan M. Hajeb et al. https://doi.org/10.3390/app10248932
- Data collection tools for post-disaster damage assessment of building and lifeline infrastructure systems J. Lozano & I. Tien https://doi.org/10.1016/j.ijdrr.2023.103819
- Artificial intelligence-empowered point cloud technologies for rapid resilience assessment of urban infrastructure under environment extremes: A comprehensive review Y. Tang et al. https://doi.org/10.1016/j.engappai.2026.114086
- Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami L. Moya et al. https://doi.org/10.1016/j.rse.2020.111743
- A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery M. Ji et al. https://doi.org/10.3390/rs11101202
- Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images B. Kalantar et al. https://doi.org/10.3390/rs12213529
- DAMAGE ESTIMATION OF BUILDINGS’ ROOFS DUE TO THE 2019 TYPHOON FAXAI USING THE POST-EVENT AERIAL PHOTOGRAPHS W. LIU & Y. MARUYAMA https://doi.org/10.2208/jscejhe.76.1_166
- Tsunami Damage Detection with Remote Sensing: A Review S. Koshimura et al. https://doi.org/10.3390/geosciences10050177
- Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment S. Al Shafian & D. Hu https://doi.org/10.3390/buildings14082344
- Early estimation of ground displacements and building damage after seismic events using SAR and LiDAR data: The case of the Amatrice earthquake in central Italy, on 24th August 2016 L. Saganeiti et al. https://doi.org/10.1016/j.ijdrr.2020.101924
- Applications of artificial intelligence for disaster management W. Sun et al. https://doi.org/10.1007/s11069-020-04124-3
- Statistical analysis of earthquake debris extent from wood‐frame buildings and its use in road networks in Japan L. Moya et al. https://doi.org/10.1177/8755293019892423
- DS-Net: A dedicated approach for collapsed building detection from post-event airborne point clouds H. Xiu et al. https://doi.org/10.1016/j.jag.2022.103150
- Quantitative assessment of earthquake-induced building damage at regional scale using LiDAR data F. Foroughnia et al. https://doi.org/10.1016/j.ijdrr.2024.104403
- Applications of Artificial Intelligence and Future Research Directions for Radiation Emergency Response Y. Park et al. https://doi.org/10.7232/JKIIE.2022.48.6.626
- Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods L. Moya et al. https://doi.org/10.3390/rs11192320
- Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada M. Kucharczyk & C. Hugenholtz https://doi.org/10.5194/nhess-19-2039-2019
- Digitalization and Spatial Documentation of Post-Earthquake Temporary Housing in Central Italy: An Integrated Geomatic Approach Involving UAV and a GIS-Based System I. Tonti et al. https://doi.org/10.3390/drones7070438
- A rapid evaluation method of the seismic damage to buildings based on UAV images D. Jia et al. https://doi.org/10.1016/j.geomat.2024.100006
- Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence A. Portillo & L. Moya https://doi.org/10.3390/rs15112754
- DALi-CGAN: domain adaptive LiDAR-based conditional generative adversarial network for earthquake damage assessment P. Talreja & S. Durbha https://doi.org/10.1080/19475705.2026.2659167
- Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data Y. Nie et al. https://doi.org/10.3390/rs13061146
- Non-Contact damage detection of drainage wells during the Mj7.6 Noto Peninsula earthquake by UAV-LiDAR technique K. Shibano & T. Suzuki https://doi.org/10.1016/j.measurement.2025.117610
- Airborne LiDAR for post-disaster recovery in Japan – lessons from coseismic landslides and lifeline impacts in the aftermath of the 1 January 2024 Noto Peninsula earthquake C. Gomez https://doi.org/10.1144/gh2025-10
- Collapsed Building Detection Using 3D Point Clouds and Deep Learning H. Xiu et al. https://doi.org/10.3390/rs12244057
- Use of Multi-Temporal LiDAR Data to Extract Collapsed Buildings and to Monitor Their Removal Process after the 2016 Kumamoto Earthquake F. Yamazaki et al. https://doi.org/10.3390/rs14235970
- Building Damage Assessment Using Feature Concatenated Siamese Neural Network M. Ramadhan et al. https://doi.org/10.1109/ACCESS.2024.3361287
- New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images Y. Endo et al. https://doi.org/10.3390/rs10122059
- Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami Y. Bai et al. https://doi.org/10.3390/rs10101626
- Data Collection Using Terrestrial Laser Scanners from the Shake-Table Test of a Full-Scale Reinforced Concrete Building P. Calvi et al. https://doi.org/10.1061/JSENDH.STENG-12627
- DETECTION OF DAMAGED BUILDINGS BECAUSE OF AN EARTHQUAKE BASED ON DEEP LEARNING OF AERIAL LASER SURVEY DATA A. KAGOSHIMA et al. https://doi.org/10.2208/jscejj.22-13020
- LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems E. Kaartinen et al. https://doi.org/10.3390/s22124610
- Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data W. Liu et al. https://doi.org/10.3390/rs11192292
- Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification L. Moya et al. https://doi.org/10.1109/TGRS.2020.3046004
Saved (final revised paper)
Latest update: 03 Jun 2026
Short summary
On 14 April 2016, an Mw 6.5 earthquake occurred in Kumamoto prefecture, Japan (foreshock). About 28 h later, another earthquake of Mw 7.0 occurred (mainshock). The earthquake produced extensive losses to the infrastructure. This paper shows the extraction of collapsed buildings from a pair of airborne lidar data recorded before and after the mainshock. A number of methods were applied and their performances were evaluated by comparison with actual data obtained from a field survey.
On 14 April 2016, an Mw 6.5 earthquake occurred in Kumamoto prefecture, Japan (foreshock). About...
Altmetrics
Final-revised paper
Preprint