Abstract
Natural disasters possess the capacity to cause substantial and extensive harm, resulting in noteworthy economic ramifications. Interestingly, there has been a noticeable increase in the amount of loss and damage brought on by these occurrences in recent years. As such, disaster management organizations have an even greater need to proactively protect communities through the development of efficient management plans. Artificial intelligence (AI) approaches have been used in a number of research projects to analyze catastrophe-related data, improving the caliber of decision-making related to disaster management. The volume and diversity of data from satellite photography make it difficult to comprehend, despite the large amount of data it offers for a variety of uses. Manual ground inspections are usually required for damage assessment, which is a time-consuming and ineffective procedure. To address these issues, this work presents a novel deep learning algorithm for classifying buildings in satellite photos as damaged or undamaged.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Patel, A. K., & Jain, D. (2022). Disaster Risks and Management In India: A Critical Analysis of the Disaster Management Act. 5thWorld Congress on Disaster Management, 300–310. https://doi.org/10.4324/9781003341956-39
Xu, J. Z., Lu, W., Li, Z., Khaitan, P., & Zaytseva, V. (2019). Building damage detection in satellite imagery using convolutional neural networks. arXiv preprint arXiv:1910.06444.
Tilon, S., Nex, F., Kerle, N., & Vosselman, G. (2020). Post-disaster building damage detection from earth observation imagery using unsupervised and transferable anomaly detecting generative adversarial networks. Remote sensing, 12(24), 4193.
Gupta, A., Watson, S., & Yin, H. (2021). Deep learning-based aerial image segmentation with open data for disaster impact assessment. Neurocomputing, 439, 22-33.
Balakumar D and Rangaraj J, “A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks”, Journal of Machine and Computing, vol. 3, no. 4, pp. 360–378, October 2023. https://doi.org/10.53759/7669/jmc202303031.
S. R and A. H, “Adaptive fuzzy logic inspired path longevity factor-based forecasting model reliable routing in MANETs,” Sensors International, vol. 3, p. 100201, 2022, https://doi.org/10.1016/j.sintl.2022.100201.
R. Subha, A. Haldorai, and A. Ramu, “Artificial Intelligence Model for Software Reusability Prediction System,” Intelligent Automation and Soft Computing, vol. 35, no. 3, pp. 2639–2654, 2023, https://doi.org/10.32604/iasc.2023.028153.
Ali-Кhusein, “An Analysis of Multi Agent Systems Agent Based Programming”, Journal of Computing and Natural Science, vol. 3, no. 4, pp. 182–193, October 2023. https://doi.org/10.53759//181X/JCNS/202303017.
Duarte, D. (2018). Nex F Kerle N Vosselman G. Multi-resolution feature fusion for image classification of building damages with convolutional neural networks Remote Sens, 10(1636), 10–3390.
Gupta, R., & Shah, M. (2021, January). Rescuenet: Joint building segmentation and damage assessment from satellite imagery. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 4405–4411). IEEE.
Mei, J., Zheng, Y. B., & Cheng, M. M. (2023). D2ANet: Difference-aware attention network for multi-level change detection from satellite imagery. Computational Visual Media, 9(3), 563–579.
Shen, Y., Zhu, S., Yang, T., Chen, C., Pan, D., Chen, J., … & Du, Q. (2021). Bdanet: Multiscale convolutional neural network with cross-directional attention for building damage assessment from satellite images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14.
Gunturu, V. R. (2022). GIS, Remote Sensing and Drones for Disaster Risk Management. 5thWorld Congress on Disaster Management, 182–194. https://doi.org/10.4324/9781003341956-26
Mohamed Shaluf, I. (2007). An overview on disasters. Disaster Prevention and Management: An International Journal, 16(5), 687–703. https://doi.org/10.1108/09653560710837000
Gevaert, C. M., Carman, M., Rosman, B., Georgiadou, Y., & Soden, R. (2021). Fairness and accountability of AI in disaster risk management: Opportunities and challenges. Patterns, 2(11), 100363. https://doi.org/10.1016/j.patter.2021.100363
Nunavath, V., & Goodwin, M. (2019). The Use of Artificial Intelligence in Disaster Management - A Systematic Literature Review. 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM). https://doi.org/10.1109/ict-dm47966.2019.9032935
Subhashini, R., Thomas, J. J., Sivasangari, A., Mohana, P., Vigneshwari, S., & Asha, P. (2022). Artificial intelligence–based intelligent geospatial analysis in disaster management. Advances of Artificial Intelligence in a Green Energy Environment, 203–221. https://doi.org/10.1016/b978-0-323-89785-3.00006-2
Bouchard, I., Rancourt, M. È., Aloise, D., & Kalaitzis, F. (2022). On Transfer Learning for Building Damage Assessment from Satellite Imagery in Emergency Contexts. Remote Sensing, 14(11), 2532.
Dolce, M., & Goretti, A. (2015). Building damage assessment after the 2009 Abruzzi earthquake. Bulletin of Earthquake Engineering, 13(8), 2241–2264.
Deng, L., & Wang, Y. (2022). Post-disaster building damage assessment based on improved U-Net. Scientific Reports, 12(1), 15862.
Anandakumar Haldorai, “A Survey of Renewable Energy Sources and their Contribution to Sustainable Development”, Journal of Enterprise and Business Intelligence, vol. 2, no. 4, pp. 211–222, October 2022. https://doi.org/10.53759/5181/JEBI202202021.
Toshihiro Endo, “Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models”, Journal of Robotics Spectrum, vol. 1, pp. 001–012, 2023. https://doi.org/10.53759/9852/JRS202301001.
Cheng, C. S., Behzadan, A. H., & Noshadravan, A. (2021). Deep learning for post hurricane aerial damage assessment of buildings. Computer Aided Civil and Infrastructure Engineering, 36(6), 695–710.
https://eleks.com/research/deep-learning-for-damage-detection-using-satellite-images/
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2024 European Alliance for Innovation
About this chapter
Cite this chapter
Haldorai, A., Babitha Lincy, R., Suriya, M., Balakrishnan, M. (2024). Crisis Assessment Through Satellite Footage Using Deep Learning Techniques for Efficient Disaster Response. In: Artificial Intelligence for Sustainable Development. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-53972-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-53972-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53971-8
Online ISBN: 978-3-031-53972-5
eBook Packages: EngineeringEngineering (R0)