{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T11:44:37Z","timestamp":1776426277765,"version":"3.51.2"},"reference-count":40,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rapid progress on disaster detection and assessment has been achieved with the development of deep-learning techniques and the wide applications of remote sensing images. However, it is still a great challenge to train an accurate and robust disaster detection network due to the class imbalance of existing data sets and the lack of training data. This paper aims at synthesizing disaster remote sensing images with multiple disaster types and different building damage with generative adversarial networks (GANs), making up for the shortcomings of the existing data sets. However, existing models are inefficient in multi-disaster image translation due to the diversity of disaster and inevitably change building-irrelevant regions caused by directly operating on the whole image. Thus, we propose two models: disaster translation GAN can generate disaster images for multiple disaster types using only a single model, which uses an attribute to represent disaster types and a reconstruction process to further ensure the effect of the generator; damaged building generation GAN is a mask-guided image generation model, which can only alter the attribute-specific region while keeping the attribute-irrelevant region unchanged. Qualitative and quantitative experiments demonstrate the validity of the proposed methods. Further experimental results on the damaged building assessment model show the effectiveness of the proposed models and the superiority compared with other data augmentation methods.<\/jats:p>","DOI":"10.3390\/rs13214284","type":"journal-article","created":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T21:42:05Z","timestamp":1635198125000},"page":"4284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["DisasterGAN: Generative Adversarial Networks for Remote Sensing Disaster Image Generation"],"prefix":"10.3390","volume":"13","author":[{"given":"Xue","family":"Rui","sequence":"first","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Xin","family":"Yuan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Yu","family":"Kang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China"},{"name":"Department of Automation, University of Science and Technology of China, Hefei 230026, China"},{"name":"Institute of Advanced Technology, University of Science and Technology of China, Hefei 230088, China"}]},{"given":"Weiguo","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"key":"ref_1","unstructured":"Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., ChoseT, H., and Gaston, M. (2019, January 16\u201320). Creating xBD: A dataset for assessing building damage from satellite imagery. Proceedings of the Computer Vision and Pattern Recognition Conference Workshops, Long Beach, CA, USA."},{"key":"ref_2","unstructured":"Shen, Y., Zhu, S., Yang, T., and Chen, C. (2020, January 6\u201312). Cross-Directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery. Proceedings of the Neural Information Processing Systems Workshops, Vancouver, BC, Canada."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hao, H., Baireddy, S., Bartusiak, E.R., Konz, L., and Delp, E.J. (2020). An Attention-Based System for Damage Assessment Using Satellite Imagery. arXiv.","DOI":"10.1109\/IGARSS47720.2021.9554054"},{"key":"ref_4","unstructured":"Boin, J.B., Roth, N., Doshi, J., Llueca, P., and Borensztein, N. (2020, January 6\u201312). Multi-class segmentation under severe class imbalance: A case study in roof damage assessment. Proceedings of the Neural Information Processing Systems Workshops, Vancouver, BC, Canada."},{"key":"ref_5","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 13). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., and Choo, J. (2018, January 18\u201322). StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation. Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"EnlightenGAN: Deep Light Enhancement Without Paired Supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lee, Y.-H., and Lai, S.-H. (2020, January 23\u201328). ByeGlassesGAN: Identity Preserving Eyeglasses Removal for Face Images. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58526-6_15"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, G., Kan, M., Shan, S., and Chen, X. (2018, January 8\u201314). Generative Adversarial Network with Spatial Attention for Face Attribute Editing. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01231-1_26"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Choi, Y., Uh, Y., Yoo, J., and Jung, W.H. (2020, January 16\u201320). StarGAN v2: Diverse Image Synthesis for Multiple Domains. Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073659","article-title":"Globally and locally consistent image completion","volume":"36","author":"Iizuka","year":"2017","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_13","unstructured":"Mounsaveng, S., Vazquez, D., Ayed, I.B., and Pedersoli, M. (2019). Adversarial Learning of General Transformations for Data Augmentation. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Liang, Z., Zheng, Z., Li, S., and Yang, Y. (2018, January 18\u201322). Camera Style Adaptation for Person Re-identification. Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00541"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Huang, S.W., Lin, C.T., and Chen, S.P. (2018, January 8\u201314). AugGAN: Cross Domain Adaptation with GAN-based Data Augmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_44"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2744","DOI":"10.1049\/iet-ipr.2018.6588","article-title":"PixTextGAN: Structure aware text image synthesis for license plate recognition","volume":"13","author":"Wu","year":"2019","journal-title":"IET Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2021.07.007","article-title":"A deep translation (GAN) based change detection network for optical and SAR remote sensing images","volume":"179","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Benjdira, B., Bazi, Y., Koubaa, A., and Ouni, K. (2019). Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images. Remote Sens., 11.","DOI":"10.3390\/rs11111369"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.isprsjprs.2020.07.001","article-title":"Weakly-supervised domain adaptation for built-up region segmentation in aerial and satellite imagery","volume":"167","author":"Iqbal","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","unstructured":"Li, Z., Wu, X., Usman, M., Tao, R., Xia, P., Chen, H., and Li, B. (2020). A Systematic Survey of Regularization and Normalization in GANs. arXiv."},{"key":"ref_21","unstructured":"Li, Z., Xia, P., Tao, R., Niu, H., and Li, B. (2020). Direct Adversarial Training: An Adaptive Method to Penalize Lipschitz Continuity of the Discriminator. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_23","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., and Zhou, T. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"132594","DOI":"10.1109\/ACCESS.2019.2941272","article-title":"ResAttr-GAN: Unpaired deep residual attributes learning for multi-domain face image translation","volume":"7","author":"Tao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Federal Emergency Management Agency (2021, October 21). Damage assessment operations manual: A guide to assessing damage and impact. Technical report, Federal Emergency Management Agency, Apr. 2016, Available online: https:\/\/www.fema.gov\/sites\/default\/files\/2020-07\/Damage_Assessment_Manual_April62016.pdf."},{"key":"ref_27","unstructured":"Federal Emergency Management Agency (2021, October 21). Hazus Hurricane Model Uer Guidance. Technical Report, Federal Emergency Management Agency, Apr. 2018, Available online: https:\/\/www.fema.gov\/sites\/default\/files\/2020-09\/fema_hazus_hurricane_user-guidance_4.2.pdf."},{"key":"ref_28","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, Australia."},{"key":"ref_29","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017, January 4\u201310). Improved training of wasserstein gans. Proceedings of the Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_30","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_31","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017, January 4\u201310). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Proceedings of the Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_32","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_33","unstructured":"Daudt, R.C., Le, S.B., and Boulch, A. (2018, January 7\u201310). Fully convolutional siamese networks for change detection. Proceedings of the IEEE International Conference on Image Processing (ICIP), Athens, Greece."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bosch, M., Conroy, C., Ortiz, B., and Bogden, P. (2020, January 21\u201325). Improving emergency response during hurricane season using computer vision. Proceedings of the SPIE Remote Sensing, Online.","DOI":"10.1117\/12.2574639"},{"key":"ref_35","unstructured":"Benson, V., and Ecker, A. (2020). Assessing out-of-domain generalization for robust building damage detection. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_37","unstructured":"Devries, T., and Taylor, G.W. (2017). Improved Regularization of Convolutional Neural Networks with Cutout. arXiv."},{"key":"ref_38","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2018). Mixup: Beyond Empirical Risk Minimization. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (2019). CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features. arXiv.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref_40","unstructured":"Chen, P., Liu, S., Zhao, H., and Jia, J. (2020). GridMask Data Augmentation. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4284\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:22:59Z","timestamp":1760167379000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,25]]},"references-count":40,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214284"],"URL":"https:\/\/doi.org\/10.3390\/rs13214284","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,25]]}}}