Post-disaster Recovery Monitoring based on Multi-Source Remote Sensing Imagery and Deep Learning

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K14261
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 22050:Civil engineering plan and transportation engineering-related
• Research Institution: The University of Tokyo
• Principal Investigator: Guo Zhiling 東京大学, 空間情報科学研究センター, 客員研究員 (40897716)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: Deep Learning / Geoscience

Outline of Final Research Achievements

Improved monitoring of post-disaster recovery is important, especially in Japan where natural disasters cause heavy losses frequently. Recently, machine learning and remote sensing data have been successfully applied in different aspects of disaster risk management. However, the monitoring in the recovery phase has not been paid sufficient attention by empirical studies, and the high-performance monitoring remains a formidable challenge. Focusing on the perspective of physical recovery, this research proposed to achieve accurate, rapid, and cost-effective post-disaster recovery monitoring based on multi-source remote sensing imagery and deep learning methods. Three stages are composed in the proposed method: 1) weak supervision for multi-task urban mapping, 2) end-to-end change detection via data fusion, 3) transfer learning based recovery monitoring. The method of this research attempts to promote the capability of pre-disaster recovery planning, evaluation, and management.

Free Research Field

Remote Sensing

Academic Significance and Societal Importance of the Research Achievements

この研究は、日本における災害後の復旧監視を向上させるため、多元なリモートセンシングとディープラーニングを応用して正確で迅速かつ費用効果の高い解決策を提供します。新しい3段階の手法を用いて、復旧フェーズに取り組み、災害復興計画と管理を改善します。