| 研究課題/領域番号 |
23K13419
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分22050:土木計画学および交通工学関連
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| 研究機関 | 東北大学 |
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研究代表者 |
袁 巍 東北大学, 災害科学国際研究所, 准教授 (60837475)
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 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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| キーワード | Remote Sensing / Urban Mapping / Vision Transformer / DOM Generation / Depth Completion / Road Network Detection / Dense Image Matching / Graph Encoding / Photogrammetry / Change Detection / Data Fusion / Disaster assesment |
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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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| 研究実績の概要 |
This year, I initiated the research on Multi-task learning based post-disaster mapping via multi-modal remote sensing observations.I have achieved several publications: for the initial raw data processing,two papers on utilizing aerial and satellite image for DOM generation,mosaicking has been published on Geo-Spatial Information Science and Remote Sensing, and one paper utilizing deep diffusion model for few-shot depth information completion are published in CVPR 2023. For the urban scene mapping part,one paper on utilizing multi-constraint vision transformer for the urban building mapping has been pulished in IEEE J-STARS and two papers on deep neural network based urban infrastructure mapping are published in IGARSS 2023.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
This year, I have moved smoothly on the developing the Multi-task learning based post-disaster mapping via multi-modal remote sensing observations. Although the complete post-disaster mapping is not finished yet, but most of the core components have been researched and initial methods such as DOM generation and urban mapping have been developed.The finished work has been published in prestigious conferences and journals.
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| 今後の研究の推進方策 |
In the second year of this project, I planned to further conduct relevant research on the multi-modal remote sensing observation based urban mapping and 3D information generation as planned, and also try to have a better integration of these methods for post-disaster assesment such as change detection and pusedo earth observation data generation.
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