Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models
Author:
Affiliation:
1. School of Mathematic& Computer Science, Wuhan Polytechnic University, Wuhan, China
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Funder
Natural Science Foundation of Hubei Province of China
National Natural Science Foundation of China Major Program
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences
Link
https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2277163
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1. Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks
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4. Usability of aerial video footage for 3-D scene reconstruction and structural damage assessment
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1. Rapid post-disaster assessment of residential buildings using Unmanned Aerial Vehicles;International Journal of Disaster Risk Reduction;2024-09
2. A Review of Using Deep Learning Technology in the Built Environment of Disaster Management Phases;Mimarlık Bilimleri ve Uygulamaları Dergisi (MBUD);2024-01-23
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