{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T01:22:59Z","timestamp":1789694579000,"version":"4.0.1"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Rapid damage identification and classification in disastrous situations and natural disasters are crucial for efficiently directing aid and resources. With the development of deep learning techniques and the availability of imagery content on social media platforms, extensive research has focused on damage assessment. Through the use of geospatial data related to such incidents, the visual characteristics of these images can quickly determine the safety situation in the region. However, training accurate disaster classification models has proven to be challenging due to the lack of labeled imagery data in this domain. This paper proposes a disaster classification framework, which combines a set of synthesized diverse disaster images generated using generative adversarial networks (GANs) and domain-specific fine-tuning of a deep convolutional neural network (CNN)-based model. The proposed model utilizes bootstrap aggregating (bagging) to further stabilize the target predictions. Since past work in this domain mainly suffers from limited data resources, a sample dataset that highlights the issue of imbalanced classification of multiple natural disasters was constructed and augmented. Qualitative and quantitative experiments show the validity of the data augmentation method employed in producing a balanced dataset. Further experiments with various evaluation metrics verified the proposed framework\u2019s accuracy and generalization ability across different classes for the task of disaster classification in comparison to other state-of-the-art techniques. Furthermore, the framework outperforms the other models by an average validation accuracy of 11%. These results provide a deep learning solution for real-time disaster monitoring systems to mitigate the loss of lives and properties.<\/jats:p>","DOI":"10.3390\/ijgi12060245","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T01:59:51Z","timestamp":1687139991000},"page":"245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Efficient Classification of Imbalanced Natural Disasters Data Using Generative Adversarial Networks for Data Augmentation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3572-8670","authenticated-orcid":false,"given":"Rokaya","family":"Eltehewy","sequence":"first","affiliation":[{"name":"Computer Engineering Department, College of Engineering & Technology, Arab Academy for Science & Technology (AAST), Cairo 2033, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Abouelfarag","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Arab Academy for Science & Technology (AAST), El Alamein 51718, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1289-0501","authenticated-orcid":false,"given":"Sherine Nagy","family":"Saleh","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, College of Engineering & Technology, Arab Academy for Science & Technology (AAST), Alexandria 1029, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1007\/s11069-021-04528-9","article-title":"Social media information sharing for natural disaster response","volume":"107","author":"Dong","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alam, F., Ofli, F., Imran, M., Alam, T., and Qazi, U. (2020, January 7\u201310). Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response. Proceedings of the 2020 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), The Hague, The Netherlands.","DOI":"10.1109\/ASONAM49781.2020.9381294"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103858","DOI":"10.1016\/j.earscirev.2021.103858","article-title":"Deep learning for geological hazards analysis: Data, models, applications, and opportunities","volume":"223","author":"Ma","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"46538","DOI":"10.1109\/ACCESS.2022.3170897","article-title":"A Deep Attentive Multimodal Learning Approach for Disaster Identification from Social Media Posts","volume":"10","author":"Hossain","year":"2022","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Aamir, M., Ali, T., Irfan, M., Shaf, A., Azam, M.Z., Glowacz, A., Brumercik, F., Glowacz, W., Alqhtani, S., and Rahman, S. (2021). Natural disasters intensity analysis and classification based on multispectral images using multi-layered deep convolutional neural network. Sensors, 21.","DOI":"10.3390\/s21082648"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"60","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_8","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (November, January 27). Cutmix: Regularization strategy to train strong classifiers with localizable features. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_9","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Olteanu, A., Castillo, C., Diaz, F., and Vieweg, S. (2014, January 1\u20134). Crisislex: A lexicon for collecting and filtering microblogged communications in crises. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, Ann Arbor, MI, USA.","DOI":"10.1609\/icwsm.v8i1.14538"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"54","DOI":"10.3389\/frobt.2016.00054","article-title":"Image classification to support emergency situation awareness","volume":"3","author":"Lagerstrom","year":"2016","journal-title":"Front. Robot. AI"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yang, W., Zhang, X., and Luo, P. (2021). Transferability of convolutional neural network models for identifying damaged buildings due to earthquake. Remote Sens., 13.","DOI":"10.3390\/rs13030504"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Alam, F., Ofli, F., and Imran, M. (2018, January 25\u201328). Crisismmd: Multimodal twitter datasets from natural disasters. Proceedings of the Twelfth International AAAI Conference on Web and Social Media, Palo Alto, CA, USA.","DOI":"10.1609\/icwsm.v12i1.14983"},{"key":"ref_14","unstructured":"Bischke, B., Helber, P., Schulze, C., Srinivasan, V., Dengel, A., and Borth, D. (2017, January 13\u201315). The Multimedia Satellite Task at MediaEval 2017. Proceedings of the MediaEval, Dublin, Ireland."},{"key":"ref_15","unstructured":"Benjamin, B., Patrick, H., Zhengyu, Z., and Damian, B. (2018, January 29\u201331). The multimedia satellite task at mediaeval 2018: Emergency response for flooding events. Proceedings of the MediaEval, Sophia Antipolis, France."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Valdez, D.B., and Godmalin, R.A.G. (2021, January 15\u201317). A Deep Learning Approach of Recognizing Natural Disasters on Images Using Convolutional Neural Network and Transfer Learning. Proceedings of the International Conference on Artificial Intelligence and its Applications, Suzhou, China.","DOI":"10.1145\/3487923.3487927"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hong, Z., Zhong, H., Pan, H., Liu, J., Zhou, R., Zhang, Y., Han, Y., Wang, J., Yang, S., and Zhong, C. (2022). Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images. Sensors, 22.","DOI":"10.3390\/s22155920"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liang, T., Lin, G., Wan, M., Li, T., Ma, G., and Lv, F. (2022, January 19\u201320). Expanding Large Pre-Trained Unimodal Models with Multimodal Information Injection for Image-Text Multimodal Classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01505"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"30437","DOI":"10.1007\/s11042-022-12869-1","article-title":"Generalization of convolutional network to domain adaptation network for classification of disaster images on Twitter","volume":"81","author":"Khattar","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shahbazi, M., Huang, Z., Paudel, D.P., Chhatkuli, A., and Van Gool, L. (2021, January 20\u201325). Efficient conditional gan transfer with knowledge propagation across classes. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01199"},{"key":"ref_21","first-page":"6977","article-title":"The effect of loss function on conditional generative adversarial networks","volume":"34","author":"Abushariah","year":"2022","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_22","first-page":"1","article-title":"Generative adversarial networks in computer vision: A survey and taxonomy","volume":"54","author":"Wang","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ma, F., Li, Y., Ni, S., Huang, S.-L., and Zhang, L. (2022). Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN. Appl. Sci., 12.","DOI":"10.3390\/app12010527"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100779","DOI":"10.1016\/j.imu.2021.100779","article-title":"Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images","volume":"27","author":"Motamed","year":"2021","journal-title":"Inform. Med. Unlocked"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rui, X., Cao, Y., Yuan, X., Kang, Y., and Song, W. (2021). DisasterGAN: Generative Adversarial Networks for Remote Sensing Disaster Image Generation. Remote Sens., 13.","DOI":"10.3390\/rs13214284"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, C., Wang, Z., and Mao, S. (2022, January 12\u201314). RFPose-GAN: Data Augmentation for RFID Based 3D Human Pose Tracking. Proceedings of the 2022 IEEE 12th International Conference on RFID Technology and Applications (RFID-TA), Cagliari, Italy.","DOI":"10.1109\/RFID-TA54958.2022.9924133"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103329","DOI":"10.1016\/j.cviu.2021.103329","article-title":"Pros and cons of GAN evaluation measures: New developments","volume":"215","author":"Borji","year":"2022","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zahid, Y., Tahir, M.A., and Durrani, M.N. (2020, January 25\u201328). Ensemble learning using bagging and inception-V3 for anomaly detection in surveillance videos. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICIP40778.2020.9190673"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_31","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_35","unstructured":"Mouzannar, H., Rizk, Y., and Awad, M. (2018, January 20\u201323). Damage Identification in Social Media Posts using Multimodal Deep Learning. Proceedings of the ISCRAM, Rochester, NY, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Ofli, F., Imran, M., and Mitra, P. (August, January 31). Damage assessment from social media imagery data during disasters. Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, Sydney, Australia.","DOI":"10.1145\/3110025.3110109"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw."},{"key":"ref_38","unstructured":"Buda, M., Maki, A., and Mazurowski, M.A. (2018). A note on the inception score. arXiv."},{"key":"ref_39","unstructured":"Yu, Y., Zhang, W., and Deng, Y. (2021). Frechet Inception Distance (FID) for Evaluating GANs, China University of Mining Technology Beijing Graduate School."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/245\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:55:44Z","timestamp":1760126144000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,17]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["ijgi12060245"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12060245","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,17]]}}}