Identification of undamaged buildings after the event of disaster using Deep Learning

Author:

Tyagi Neha1,Saraswat Mukesh1

Affiliation:

1. Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, India

Publisher

ACM

Reference33 articles.

1. Waleed Alsabhan and Turky Alotaiby . 2022. Automatic Building Extraction on Satellite Images Using Unet and ResNet50. Computational Intelligence and Neuroscience 2022 (feb 2022 ), 1–12. https://doi.org/10.1155/2022/5008854 10.1155/2022 Waleed Alsabhan and Turky Alotaiby. 2022. Automatic Building Extraction on Satellite Images Using Unet and ResNet50. Computational Intelligence and Neuroscience 2022 (feb 2022), 1–12. https://doi.org/10.1155/2022/5008854

2. Sean Andrew Chen Andrew Escay Christopher Haberland Tessa Schneider Valentina Staneva and Youngjun Choe. 2018. Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery. https://doi.org/10.48550/ARXIV.1812.05581 10.48550/ARXIV.1812.05581

3. Sean Andrew Chen Andrew Escay Christopher Haberland Tessa Schneider Valentina Staneva and Youngjun Choe. 2018. Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery. https://doi.org/10.48550/ARXIV.1812.05581

4. Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives

5. Laigen Dong and Jie Shan . 2013. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing 84 (10 2013 ), 85–99. https://doi.org/10.1016/j.isprsjprs.2013.06.011 10.1016/j.isprsjprs.2013.06.011 Laigen Dong and Jie Shan. 2013. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing 84 (10 2013), 85–99. https://doi.org/10.1016/j.isprsjprs.2013.06.011

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1. Deep Learning Models for Hazard-Damaged Building Detection Using Remote Sensing Datasets: A Comprehensive Review;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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