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

• 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: 郭 直霊 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
• Project Status: Granted (Fiscal Year 2021)
• Budget Amount: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000); Fiscal Year 2023: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000); Fiscal Year 2022: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000); Fiscal Year 2021: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
• Keywords: Post-disaster Monitoring / Change Detection / Deep Learning / Remote Sensing

Outline of Research at the Start

This research addresses the challenge of achieving high performance recovery monitoring and related tasks based on inexact and inadequate training dataset. The weak-supervised learning, multi-task learning, transfer learning is proposed.
This research sets up an architecture which makes connecting between the research fields of urban mapping, change detection, and recovery monitoring.
Based on this research, more interdisciplinary datasets and methods are expected to for post-disaster recovery monitoring optimization to promote the efficiency and accuracy.