Enhancing Post-Disaster Damage Detection and Recovery Monitoring by Addressing Class Imbalance in Satellite Imagery Using Enhanced Super-Resolution GANs (ESRGAN)
Access to very high-resolution (HR) satellite imagery is often limited, delayed, or cost-prohibitive, restricting accurate and timely post-disaster damage detection and recovery monitoring (PDDRM). Additionally, class imbalance in disaster classification datasets further complicates deep learning (D...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/853/2025/isprs-archives-XLVIII-G-2025-853-2025.pdf |
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