Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-789-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-789-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning
Anirudh Rao
CORRESPONDING AUTHOR
Seismic Risk Team, Global Earthquake Model Foundation, Pavia, Italy
Jungkyo Jung
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Vitor Silva
Seismic Risk Team, Global Earthquake Model Foundation, Pavia, Italy
Giuseppe Molinario
World Bank Group, Washington, DC, USA
Sang-Ho Yun
Earth Observatory of Singapore, Nanyang Technological University, Singapore
Asian School of the Environment, Nanyang Technological University, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
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Eleanor Tennant, Susanna F. Jenkins, Victoria Miller, Richard Robertson, Bihan Wen, Sang-Ho Yun, and Benoit Taisne
Nat. Hazards Earth Syst. Sci., 24, 4585–4608, https://doi.org/10.5194/nhess-24-4585-2024, https://doi.org/10.5194/nhess-24-4585-2024, 2024
Short summary
Short summary
After a volcanic eruption, assessing building damage quickly is important for responding to and recovering from the disaster. Traditional damage assessment methods such as ground surveys can be time-consuming and resource-intensive, hindering rapid response and recovery efforts. To overcome this, we have developed an automated approach for tephra fall building damage assessment. Our approach uses drone-acquired optical images and deep learning to rapidly generate building damage data.
Irina Dallo, Michèle Marti, Nadja Valenzuela, Helen Crowley, Jamal Dabbeek, Laurentiu Danciu, Simone Zaugg, Fabrice Cotton, Domenico Giardini, Rui Pinho, John F. Schneider, Céline Beauval, António A. Correia, Olga-Joan Ktenidou, Päivi Mäntyniemi, Marco Pagani, Vitor Silva, Graeme Weatherill, and Stefan Wiemer
Nat. Hazards Earth Syst. Sci., 24, 291–307, https://doi.org/10.5194/nhess-24-291-2024, https://doi.org/10.5194/nhess-24-291-2024, 2024
Short summary
Short summary
For the release of cross-country harmonised hazard and risk models, a communication strategy co-defined by the model developers and communication experts is needed. The strategy should consist of a communication concept, user testing, expert feedback mechanisms, and the establishment of a network with outreach specialists. Here we present our approach for the release of the European Seismic Hazard Model and European Seismic Risk Model and provide practical recommendations for similar efforts.
John Douglas, Helen Crowley, Vitor Silva, Warner Marzocchi, Laurentiu Danciu, and Rui Pinho
EGUsphere, https://doi.org/10.5194/egusphere-2023-991, https://doi.org/10.5194/egusphere-2023-991, 2023
Preprint withdrawn
Short summary
Short summary
Estimates of the earthquake ground motions expected during the lifetime of a building or the length of an insurance policy are frequently calculated for locations around the world. Estimates for the same location from different studies can show large differences. These differences affect engineering, financial and risk management decisions. We apply various approaches to understand when such differences have an impact on such decisions and when they are expected because data are limited.
Cited articles
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2015 Gorkha earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20150425-Nepal_EQ/DPM/, last access: 15 February 2023a. a
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2017 Puebla earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20170919-M7.1_Raboso_Mexico_EQ/DPM/, last access: 15 February 2023b. a
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2020 Puerto Rico earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20200106-Puerto_Rico_EQ/DPM/, last access: 15 February 2023c. a
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2020 Zagreb earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20200322_Zagreb_EQ/DPM/, last access: 15 February 2023d. a
Bai, Y., Adriano, B., Mas, E., and Koshimura, S.: Machine learning based building damage mapping from the ALOS-2/PALSAR-2 SAR imagery: Case study of 2016 Kumamoto earthquake, Journal of Disaster Research, 12, 646–655, https://doi.org/10.20965/jdr.2017.p0646, 2017. a, b, c
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Brodersen, K. H., Ong, C. S., Stephan, K. E., and Buhmann, J. M.: The balanced accuracy and its posterior distribution, in: International Conference on Pattern Recognition, 23–26 August 2010, Istanbul, Turkey, IEEE, 3121–3124, https://doi.org/10.1109/ICPR.2010.764, 2010. a
Buendía Sánchez, L. M. and Angulo, E. R.: Análisis de los daños en viviendas y edificios comerciales durante la ocurrencia del sismo del 19 de septiembre de 2017, Revista de Ingeniería
Sísmica No, 101, 19–35, https://doi.org/10.18867/ris.101.508, 2017. a, b
Capella Space: SAR Imagery Products, https://www.capellaspace.com/data/sar-imagery-products/, last access: 30 December 2022. a
Copernicus Emergency Management Service and The European Commission: Copernicus rapid damage assessment and mapping service, https://emergency.copernicus.eu/mapping/ems/damage-assessment (last access: 30 December 2022), 2012. a
Cotrufo, S., Sandu, C., Giulio Tonolo, F., and Boccardo, P.: Building damage assessment scale tailored to remote sensing vertical imagery, Eur. J. Remote Sens., 51, 991–1005,
https://doi.org/10.1080/22797254.2018.1527662, 2018. a
Dell'Acqua, F. and Gamba, P.: Remote sensing and earthquake damage assessment: Experiences, limits, and perspectives, P. IEEE, 100,
2876–2890, https://doi.org/10.1109/JPROC.2012.2196404, 2012. a, b
Dong, L. and Shan, J.: A comprehensive review of earthquake-induced building damage detection with remote sensing techniques, ISPRS J. Photogramm., 84, 85–99, https://doi.org/10.1016/j.isprsjprs.2013.06.011, 2013. a
Esri: Enhanced Damage Assessment Solution Improves Collaboration,
https://www.esri.com/arcgis-blog/products/arcgis-solutions/local-government/enhanced-damage-assessment-solution-released/ (last access: 30 December 2022), 2021. a
European Sesimological Commission: European Macroseismic Scale 1998 (EMS-98), Tech. rep., Centre Europèen de Géodynamique et de Séismologie,
Luxembourg, ISBN 2-87977-008-4, 1998. a
Federal Emergency Management Agency: FEMA-4473-DR. Preliminary Damage Assessment Report, Puerto Rico Earthquakes, Tech. rep., Federal Emergency Management Agency, https://www.fema.gov/sites/default/files/2020-03/FEMA4473DRPR.pdf (last access: 30 December 2022), 2020. a
FEMA and ASCE: FEMA 356: Prestandard and commentary for the seismic
rehabilitation of buildings, Tech. rep., Federal Emergency Management
Agency, Washington, DC, ISBN 978-1484027554, 2000. a
FEMA Geospatial Resource Center: Puerto Rico Mw 6.4 Earthquake Preliminary Damage Assessments Dashboard, Federal Emergency Management Agency [data set], https://gis-fema.hub.arcgis.com/apps/FEMA::puerto-rico-m-6-4-earthquake-preliminary-damage-assessments-dashboard/explore (last access: 15 February 2023), 2020. a
Feng, Y., Zhou, M., and Tong, X.: Imbalanced classification: A paradigm-based review, Stat. Anal. Data Min., 14, 383–406,
https://doi.org/10.1002/sam.11538, 2021. a
Ge, P., Gokon, H., and Meguro, K.: Building Damage Assessment Using Intensity SAR Data with Different Incidence Angles and Longtime Interval, Journal of Disaster Research, 14, 456–465, https://doi.org/10.20965/JDR.2019.P0456, 2019. a
Ge, P., Gokon, H., and Meguro, K.: A review on synthetic aperture radar-based building damage assessment in disasters, Remote Sens. Environ., 240, 111693, https://doi.org/10.1016/j.rse.2020.111693, 2020. a, b
Gholamy, A., Kreinovich, V., and Kosheleva, O.: Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation, Departmental Technical Reports (CS), Department of Computer Science, The University of Texas at El Paso, 1–6, https://scholarworks.utep.edu/cs_techrep/1209/ (last access: 30 December 2022), 2018. a
Government of Croatia: Croatia earthquake: Rapid Damage and Needs Assessment, Tech. rep., Government of Croatia, Zagreb, Croatia, https://documents1.worldbank.org/curated/en/311901608097332728/pdf/Croatia-Earthquake-Rapid-Damage-and-Needs-Assessment-2020.pdf (last access: 15 February 2023), 2020. a
Grinsztajn, L., Oyallon, E., and Varoquaux, G.: Why do tree-based models still outperform deep learning on tabular data?, arXiv [preprint], https://doi.org/10.48550/arXiv.2207.08815, 18 July 2022. a
Humanitarian OpenStreetMap Team (HOT) and OpenStreetMap contributors: Nepal Buildings, Humanitarian Data Exchange, Humanitarian OpenStreetMap Team (HOT) and OpenStreetMap contributors [data set], https://data.humdata.org/dataset/hotosm_npl_buildings (last access: 15 February 2023), 2020a. a
Humanitarian OpenStreetMap Team (HOT) and OpenStreetMap contributors: Puerto Rico Buildings, Humanitarian Data Exchange, Humanitarian OpenStreetMap Team (HOT) and OpenStreetMap contributors [data set], https://data.humdata.org/dataset/hotosm_pri_buildings (last access: 15 February 2023), 2020b. a
ICEYE: A Revolution in Synthetic Aperture Radar (SAR) Data Earth Observation, https://www.iceye.com/hubfs/Downloadables/SAR_Data_Brochure_ICEYE.pdf, last access: 30 December 2022. a
Ji, M., Liu, L., and Buchroithner, M. F.: Identifying collapsed buildings using post-earthquake satellite imagery and convolutional neural networks: A
case study of the 2010 Haiti Earthquake, Remote Sens., 10, 1689,
https://doi.org/10.3390/rs10111689, 2018. a
Ji, M., Liu, L., Du, R., and Buchroithner, M. F.: A comparative study of
texture and convolutional neural network features for detecting collapsed
buildings after earthquakes using pre- and post-event satellite imagery,
Remote Sens., 11, 1202, https://doi.org/10.3390/rs11101202, 2019. a
Ji, M., Liu, L., Zhang, R., and Buchroithner, M. F.: Discrimination of
Earthquake-Induced Building Destruction from Space Using a Pretrained CNN
Model, Appl. Sci., 10, 602, https://doi.org/10.3390/app10020602, 2020. a
Jung, J., Kim, D. J., Lavalle, M., and Yun, S.-H.: Coherent Change Detection
Using InSAR Temporal Decorrelation Model: A Case Study for Volcanic Ash
Detection, IEEE T. Geosci. Remote, 54, 5765–5775, https://doi.org/10.1109/TGRS.2016.2572166, 2016. a
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu,
T. Y.: LightGBM: A highly efficient gradient boosting decision tree, in:
Advances in Neural Information Processing Systems, 4–9 December 2017, Long Beach, California, USA, Neural Information Processing Systems Foundation, Inc. (NeurIPS), ISBN 9781510860964, https://github.com/Microsoft/LightGBM (last access: 30 December 2022), 2017. a
Kellogg, K., Rosen, P., Barela, P., Hoffman, P., Edelstein, W., Standley, S.,
Dunn, C., Guerrero, A. M., Harinath, N., Shaffer, S., Baker, C., and
Xaypraseuth, P.: NASA-ISRO Synthetic Aperture Radar (NISAR) Mission, in:
IEEE Aerospace Conference, 7–14 March 2020, Big Sky, USA, IEEE, https://doi.org/10.1109/AERO47225.2020.9172638, 2020. a, b
Krawczyk, B., Woźniak, M., and Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification, Appl. Soft Comput. J., 14, 554–562, https://doi.org/10.1016/j.asoc.2013.08.014, 2014. a
Lee, J., Xu, J. Z., Sohn, K., Lu, W., Berthelot, D., Gur, I., Khaitan, P., Ke-Wei, Huang, Koupparis, K., and Kowatsch, B.: Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques, in: NeurIPS 2020 Artificial Intelligence for Humanitarian Assistance and Disaster
Response Workshop, 6–12 December 2020, virtual, 1–10, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.14004, 24 November 2020. a
Loos, S., Lallemant, D., Baker, J. W., McCaughey, J., Yun, S.-H., Budhathoki, N., Khan, F., and Singh, R.: G-DIF: A geospatial data integration framework
to rapidly estimate post-earthquake damage, Earthq. Spectra, 36,
1695–1718, https://doi.org/10.1177/8755293020926190, 2020. a
Mangalathu, S., Sun, H., Nweke, C. C., Yi, Z., and Burton, H. V.: Classifying Earthquake Damage to Buildings Using Machine Learning, Earthq. Spectra, 36, 183–208, https://doi.org/10.1177/8755293019878137, 2020. a, b
Microsoft: Open dataset of machine extracted buildings in Uganda and Tanzania, GitHub, https://github.com/microsoft/Uganda-Tanzania-Building-Footprints (last access: 30 December 2022), 2018a. a
Microsoft: Open dataset of machine extracted buildings in Uganda and Tanzania, Microsoft, https://www.microsoft.com/en-us/maps/building-footprints (last access: 30 December 2022), 2018b. a
Miura, H., Midorikawa, S., and Matsuoka, M.: Building damage assessment using high-resolution satellite SAR images of the 2010 Haiti earthquake, Earthq. Spectra, 32, 591–610, https://doi.org/10.1193/033014EQS042M, 2016. a
Motohka, T., Kankaku, Y., Miura, S., and Suzuki, S.: ALOS-4 L-Band SAR Observation Concept and Development Status, in: International Geoscience and
Remote Sensing Symposium (IGARSS), 26 September–2 October 2020, virtual, 3792–3794, https://doi.org/10.1109/IGARSS39084.2020.9323701, 2020. a
Moya, L., Mas, E., Adriano, B., Koshimura, S., Yamazaki, F., and Liu, W.: An integrated method to extract collapsed buildings from satellite imagery, hazard distribution and fragility curves, Int. J. Disast. Risk Re., 31, 1374–1384, https://doi.org/10.1016/j.ijdrr.2018.03.034, 2018a. a, b
Moya, L., Perez, L. R., Mas, E., Adriano, B., Koshimura, S., and Yamazaki, F.: Novel unsupervised classification of collapsed buildings using satellite
imagery, hazard scenarios and fragility functions, Remote Sens., 10, 296,
https://doi.org/10.3390/rs10020296, 2018b. a, b
National Planning Commission: Nepal earthquake 2015: Post-disaster need
assessment, Tech. rep., Government of Nepal, Kathmandu, Nepal,
https://doi.org/10.1007/978-981-13-6573-7_2, 2015. a
Natsuaki, R., Nagai, H., Tomii, N., and Tadono, T.: Sensitivity and limitation in damage detection for individual buildings using InSAR coherence – A case study in 2016 Kumamoto earthquakes, Remote Sens., 10, 245,
https://doi.org/10.3390/rs10020245, 2018. a
Nex, F., Duarte, D., Tonolo, F. G., and Kerle, N.: Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in
Operational Conditions, Remote Sens., 11, 2765, https://doi.org/10.3390/rs11232765, 2019. a, b
Office for Strategic Planning and City Development for the City of Zagreb: ZG3D: 3D model Grada Zagreba, Office for Strategic Planning and City Development for the City of Zagreb [data set], https://zagreb.gdi.net/zg3d/ (last access: 15 February 2023), 2021. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.:
Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, https://doi.org/10.4018/978-1-5225-9902-9.ch008, 2011. a
Plank, S.: Rapid damage assessment by means of multi-temporal SAR-A
comprehensive review and outlook to Sentinel-1, Remote Sens., 6,
4870–4906, https://doi.org/10.3390/rs6064870, 2014. a
Probst, P., Wright, M. N., and Boulesteix, A. L.: Hyperparameters and tuning strategies for random forest, WIRES Data Min. Knowl., 9, 1–15, https://doi.org/10.1002/widm.1301, 2019. a, b
Rao, A.: GEMScienceTools/eo-damage-detection: v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.7578961, 2023. a
Reinoso, E., Quinde, P., Buendía, L., and Ramos, S.: Intensity and
damage statistics of the September 19, 2017 Mexico earthquake: Influence of
soft story and corner asymmetry on the damage reported during the
earthquake, Earthq. Spectra, 37, 1875–1899,
https://doi.org/10.1177/8755293020981981, 2021. a, b
Roeslin, S., Ma, Q., and García, H. J.: Damage assessment on buildings following the 19th September 2017 puebla, Mexico earthquake, Frontiers in Built Environment, 4, 1–18, https://doi.org/10.3389/fbuil.2018.00072, 2018. a
Roeslin, S., Ma, Q., Juárez-Garcia, H., Gómez-Bernal, A., Wicker, J., and Wotherspoon, L.: A machine learning damage prediction model for the
2017 Puebla-Morelos, Mexico, earthquake, Earthq. Spectra, 36, 314–339,
https://doi.org/10.1177/8755293020936714, 2020. a, b
Sextos, A., De Risi, R., Pagliaroli, A., Foti, S., Passeri, F., Ausilio, E., Cairo, R., Capatti, M. C., Chiabrando, F., Chiaradonna, A., Dashti, S., De Silva, F., Dezi, F., Durante, M. G., Giallini, S., Lanzo, G., Sica, S., Simonelli, A. L., and Zimmaro, P.: Local site effects and incremental damage
of buildings during the 2016 Central Italy Earthquake sequence, Earthq.
Spectra, 34, 1639–1669, https://doi.org/10.1193/100317EQS194M, 2018. a
Sirko, W., Kashubin, S., Ritter, M., Annkah, A., Bouchareb, Y. S. E., Dauphin, Y., Keysers, D., Neumann, M., Cisse, M., and Quinn, J.: Continental-Scale Building Detection from High Resolution Satellite Imagery, arXiv [preprint], https://doi.org/10.48550/arXiv.2107.12283, 26 July 2021. a
Stephenson, O. L., Kohne, T., Zhan, E., Cahill, B. E., Yun, S.-H., Ross, Z. E., and Simons, M.: Deep Learning-Based Damage Mapping With InSAR Coherence Time Series, IEEE T. Geosci. Remote, 60, 1–17,
https://doi.org/10.1109/tgrs.2021.3084209, 2021. a
Tilon, S., Nex, F., Kerle, N., and Vosselman, G.: Post-disaster building
damage detection from earth observation imagery using unsupervised and
transferable anomaly detecting generative adversarial networks, Remote
Sensi., 12, 1–27, https://doi.org/10.3390/rs12244193, 2020. a
United Nations Development Programme: Guidance Note: Household and Building Damage Assessment (HBDA), UNDP, https://data.undp.org/wp-content/uploads/2021/10/HBDA_Handbook_English-2.pdf (last access: 16 February 2023), 2021. a
United Nations Institute for Training and Research: UNOSAT Rapid Mapping Service, https://www.unitar.org/maps/unosat-rapid-mapping-service (last access: 2 April 2022), 2003. a
University of Zagreb and The City of Zagreb: The Database of Post-Earthquake Building Usability Classification, Croatian Centre of Earthquake Engineering (HCPI – Hrvatski Centar Za Potresno Inženjerstvo), Faculty of Civil Engineering, University of Zagreb and The City of Zagreb [data set], https://www.hcpi.hr/rezultati-procjena-ostecenja-gradevina-nakon-potresa-31
(last access: 10 April 2021), 2020. a
U.S. Geological Survey: USGS ShakeMap for the 2015 Gorkha earthquake, ShakeMap – Earthquake Ground Motion and Shaking Intensity Maps: U.S. Geological Survey [data set], https://earthquake.usgs.gov/earthquakes/eventpage/us20002926/shakemap/pga (last access: 15 February 2023), 2017a. a
U.S. Geological Survey: USGS ShakeMap for the 2017 Puebla earthquake, ShakeMap – Earthquake Ground Motion and Shaking Intensity Maps: U.S. Geological Survey [data set], https://earthquake.usgs.gov/earthquakes/eventpage/us2000ar20/shakemap/pga (last access: 15 February 2023), 2017b. a
U.S. Geological Survey: USGS ShakeMap for the 2020 Puerto Rico earthquake, ShakeMap – Earthquake Ground Motion and Shaking Intensity Maps: U.S. Geological Survey [data set], https://earthquake.usgs.gov/earthquakes/eventpage/us70006vll/shakemap/pga (last access: 15 February 2023), 2017c. a
U.S. Geological Survey: USGS ShakeMap for the 2020 Zagreb earthquake, ShakeMap – Earthquake Ground Motion and Shaking Intensity Maps: U.S. Geological Survey [data set], https://earthquake.usgs.gov/earthquakes/eventpage/us70008dx7/shakemap/pga (last access: 15 February 2023), 2017d. a
Wald, D. J., Worden, C. B., Thompson, E. M., and Hearne, M. G.: ShakeMap
operations, policies, and procedures, Earthq. Spectra, 38, 756–777,
https://doi.org/10.1177/87552930211030298, 2022. a
Wieland, M., Liu, W., and Yamazaki, F.: Learning change from Synthetic Aperture Radar images: Performance evaluation of a Support Vector Machine to
detect earthquake and tsunami-induced changes, Remote Sens., 8, 792,
https://doi.org/10.3390/rs8100792, 2016. a, b
Xie, B., Xu, J., Jung, J., Yun, S.-H., Zeng, E., Brooks, E. M., Dolk, M., and
Narasimhalu, L.: Machine Learning on Satellite Radar Images to Estimate
Damages After Natural Disasters, in: 28th International Conference on
Advances in Geographic Information Systems, 3–6 November 2020, Seattle, WA, USA, edited by: Lu, C.-T., Wang, F.,
Trajcevski, G., Huang, Y., Newsam, S., and Xiong, L., 461–464,
Association for Computing Machinery, New York, NY, United States, Seattle,
Washington, https://doi.org/10.1145/3397536.3422349, 2020.
a
Xu, H., Kinfu, K. A., LeVine, W., Panda, S., Dey, J., Ainsworth, M., Peng, Y.-C., Kusmanov, M., Engert, F., White, C. M., Vogelstein, J. T., and Priebe,
C. E.: When are Deep Networks really better than Decision Forests at small
sample sizes, and how?, arXiv [preprint], https://doi.org/10.48550/arXiv.2108.13637, 31 August 2021. a
Xu, J. Z., Lu, W., Li, Z., Khaitan, P., and Zaytseva, V.: Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks, in: 33rd Conference on Neural Information Processing Systems, NeurIPS 2019, 10–14 December 2019, Vancouver, Canada, arXiv [preprint], https://doi.org/10.48550/arXiv.1910.06444, 14 October 2019. a
Yun, S.-H., Fielding, E., Webb, F., and Simons, M.: Damage Proxy Map From Interferometric Synthetic Aperture Radar Coherence, Patent Public Search, https://image-ppubs.uspto.gov/dirsearch-public/print/downloadPdf/9207318 (last access: 15 February 2023), 2015. a
Yun, S.-H., Hudnut, K. W., Owen, S., Webb, F., Simons, M., Sacco, P., Gurrola, E., Manipon, G., Liang, C., Fielding, E., Milillo, P., Hua, H., and Coletta, A.: Rapid damage mapping for the 2015 Mw 7.8 Gorkha Earthquake Using synthetic aperture radar data from COSMO-SkyMed and ALOS-2 satellites, Seismol. Res. Lett., 86, 1549–1556, https://doi.org/10.1785/0220150152,
2015c. a
Short summary
This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets including high-resolution building inventories, while also leveraging recent advances in machine-learning algorithms. For three out of the four recent earthquakes studied, the machine-learning framework is able to identify over 50 % or nearly half of the damaged buildings successfully.
This article presents a framework for semi-automated building damage assessment due to...
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