• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

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

Research Project

Project/Area Number 21K14261
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 22050:Civil engineering plan and transportation engineering-related
Research InstitutionThe 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 *help
¥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)
KeywordsPost-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.

URL: 

Published: 2021-04-28   Modified: 2021-08-30  

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi