| 配分額 *注記 |
4,550千円 (直接経費: 3,500千円、間接経費: 1,050千円)
2025年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2024年度: 1,690千円 (直接経費: 1,300千円、間接経費: 390千円)
2023年度: 1,950千円 (直接経費: 1,500千円、間接経費: 450千円)
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| 研究開始時の研究の概要 |
Automatic post-disaster damage mapping is important, especially in Japan where natural disasters cause heavy losses frequently. Focusing on the perspective of physical damage mapping, this research proposed to achieve rapid, accurate, and cost-effective post-disaster damage mapping based on multi-modal remote sensing observations (and deep learning methods. There are three main steps in the proposed methods: 1) Multi-modal remote sensing data fusion for feature extraction; 2) Progressive-supervision for multi-task urban mapping; 3) transfer learning based post-disaster damage mapping.
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