Constructing an Extensible Building Damage Dataset via Semi-supervised Fine-Tuning across 12 Natural Disasters
Post-disaster building damage assessment (BDA) is vital for emergency response. Deep learning (DL) models are increasingly being applied to achieve quick and automatic BDA on disaster remote sensing imagery, and their performance largely relies on the knowledge base offered by the dataset. However,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Journal of Remote Sensing |
| Online Access: | https://spj.science.org/doi/10.34133/remotesensing.0733 |
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