{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T20:09:49Z","timestamp":1784318989923,"version":"3.55.0"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T00:00:00Z","timestamp":1597276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods.<\/jats:p>","DOI":"10.3390\/a13080195","type":"journal-article","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T09:23:44Z","timestamp":1597310624000},"page":"195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Deep Learning-Enabled Semantic Inference of Individual Building Damage Magnitude from Satellite Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3746-3298","authenticated-orcid":false,"given":"Bradley J.","family":"Wheeler","sequence":"first","affiliation":[{"name":"Geoinformatics Laboratory, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15213, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan A.","family":"Karimi","sequence":"additional","affiliation":[{"name":"Geoinformatics Laboratory, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15213, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yu, M., Yang, C., and Li, Y. (2018). Big data in natural disaster management: A review. Geosciences, 8.","DOI":"10.3390\/geosciences8050165"},{"key":"ref_2","first-page":"683","article-title":"Buildings change detection based on shape matching for multi-resolution remote sensing imagery","volume":"42","author":"Abdessetar","year":"2017","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1080\/01431161.2016.1259673","article-title":"Building change detection after earthquake using multi-criteria decision analysis based on extracted information from high spatial resolution satellite images","volume":"38","author":"Janalipour","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","unstructured":"Doshi, J., Basu, S., and Pang, G. (2020, May 05). From Satellite Imagery to Disaster Insights. Available online: http:\/\/arxiv.org\/abs\/1812.07033."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5-1","DOI":"10.1147\/JRD.2020.2970903","article-title":"Next-Generation Geospatialtemporal Information technologies for Disaster Management","volume":"64","author":"Albrecht","year":"2020","journal-title":"IBM J. Res. Dev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8775","DOI":"10.1109\/TGRS.2019.2922908","article-title":"A multi-level semantic scene interpretation strategy for change interpretation in remote sensing imagery","volume":"57","author":"Ghazouani","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","unstructured":"Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., and Gaston, M. (2020, May 11). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. Available online: http:\/\/arxiv.org\/abs\/1911.09296."},{"key":"ref_8","unstructured":"Xu, J.Z., Lu, W., Li, Z., Khaitan, P., and Zaytseva, V. (2020, April 28). Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks. Available online: http:\/\/arxiv.org\/abs\/1910.06444."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1193\/1.1650865","article-title":"Using high-resolution satellite images for post-earthquake building damage assessment: A study following the 26 January 2001 gujarat earthquake","volume":"20","author":"Saito","year":"2004","journal-title":"Earthq. Spectra"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1080\/01431161.2019.1674463","article-title":"What is a remote sensing change detection technique? Towards a conceptual framework","volume":"41","author":"Salah","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tu, J., Li, D., Feng, W., Han, Q., and Sui, H. (2017). Detecting damaged building regions based on semantic scene change from multi-temporal high-resolution remote sensing images. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050131"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cao, Q.D., and Choe, Y. (2020). Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks. Nat. Hazards, 1\u201320.","DOI":"10.1007\/s11069-020-04133-2"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bai, Y., Mas, E., and Koshimura, S. (2018). Towards operational satellite-based damage-mapping using u-net convolutional network: A case study of 2011 tohoku earthquake-tsunami. Remote Sens., 10.","DOI":"10.3390\/rs10101626"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery","volume":"57","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_21","unstructured":"(2020, April 27). xView2. Available online: https:\/\/xview2.org\/."},{"key":"ref_22","unstructured":"(2020, April 27). Maxar. Available online: https:\/\/www.digitalglobe.com\/ecosystem\/open-data."},{"key":"ref_23","unstructured":"Singh, R.V. (2020, April 27). ImageNet Winning CNN Architectures\u2014A Review. Available online: http:\/\/rajatvikramsingh.github.io\/media\/DeepLearning_ImageNetWinners.pdf."},{"key":"ref_24","unstructured":"(2020, April 27). PyTorch Models. Available online: https:\/\/pytorch.org\/docs\/stable\/torchvision\/models.html."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_26","unstructured":"(2020, April 27). Sklearn Class Weighting. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.utils.class_weight.compute_class_weight.html."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, L., Liang, J., Weng, M., and Zhu, H. (2018). A multiple-feature reuse network to extract buildings from remote sensing imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091350"},{"key":"ref_28","unstructured":"(2020, May 15). Torchvision Transforms. Available online: https:\/\/pytorch.org\/docs\/stable\/torchvision\/transforms.html."},{"key":"ref_29","unstructured":"Brownlee, J. (2020, May 06). How to Configure the Learning Rate When Training Deep Learning Neural Networks. Machine Learning Mastery. Available online: https:\/\/machinelearningmastery.com\/learning-rate-for-deep-learning-neural-networks\/."},{"key":"ref_30","unstructured":"Kimura, M. (2020, April 27). GitHub-Motokimura\/Spacenet_Building_Detection: Project to Train\/Test Convolutional Neural Networks to Extract Buildings from Spacenet Satellite Imageries. Available online: https:\/\/github.com\/motokimura\/spacenet_building_detection."},{"key":"ref_31","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Adv. Cryptol. CRYPTO 2017"},{"key":"ref_32","unstructured":"Van Etten, A., Lindenbaum, D., and Bacastow, T.M. (2020, April 27). SpaceNet: A Remote Sensing Dataset and Challenge Series. Available online: http:\/\/arxiv.org\/abs\/1807.01232."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","article-title":"Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks","volume":"130","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sakrapee, P., Jamie, S., Pranam, J., and van den Anton, H. (2016). Semantic Labeling of Aerial and Satellite Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, Available online: https:\/\/ieeexplore-ieee-org.pitt.idm.oclc.org\/stamp\/stamp.jsp?tp=&arnumber=7516568.","DOI":"10.1109\/JSTARS.2016.2582921"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/8\/195\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:00:04Z","timestamp":1760176804000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/8\/195"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,13]]},"references-count":35,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["a13080195"],"URL":"https:\/\/doi.org\/10.3390\/a13080195","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,13]]}}}