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Deep forest for disaster monitoring with multi-temporal and multi-modal earth observation data

Research Project

Project/Area Number 19K20309
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 61010:Perceptual information processing-related
Research InstitutionInstitute of Physical and Chemical Research

Principal Investigator

XIA JUNSHI  国立研究開発法人理化学研究所, 革新知能統合研究センター, 研究員 (00830168)

Project Period (FY) 2019-04-01 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
Budget Amount *help
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Keywordsdisaster monitoring / multi-modality / deep forest / deep learning / Damage mapping / Machine learning / Multi-source datasets / Deep forest / Siamese networks / Disaster monitoring / Multi-temporal / Multi-modal / Remote sensing
Outline of Research at the Start

The outline of research is divided into three parts:
1) constructing the database of disasters;
2) developing Deep forest for disaster monitoring;
3) developing Deep transfer forest among different disasters.

Outline of Final Research Achievements

We developed methods for disaster damage mapping by using multi-temporal and multi-modal earth observation data and deep learning methods (e.g., CNN, deep forest, PU learning). We also studied different-modality learning: how to learn relevant features between two modalities. Our deep learning methods were applied to natural disaster mapping, such as the 2018 Sulawesi, the Sunda Strait earthquake/tsunami, etc., which can solve the limited training sample problem and outperform the traditional techniques.

Academic Significance and Societal Importance of the Research Achievements

本研究において、様々なセンサーから得られた地球観測データを用いて災害マッピングを調査できること、また深層学習手法を用いて分類能力を高めることの必要性について証明しました。実際には、教示データの欠如,観測データの不均一性,学習方法の能力などの次の課題により,災害被害マッピングの予測結果が低くなります。最も顕著な成果は,提案された方法がパフォーマンスを改善することが可能になったということです。

Report

(4 results)
  • 2021 Annual Research Report   Final Research Report ( PDF )
  • 2020 Research-status Report
  • 2019 Research-status Report
  • Research Products

    (12 results)

All 2022 2021 2020 2019

All Journal Article (6 results) (of which Int'l Joint Research: 4 results,  Peer Reviewed: 6 results,  Open Access: 5 results) Presentation (3 results) (of which Int'l Joint Research: 3 results,  Invited: 2 results) Funded Workshop (3 results)

  • [Journal Article] Landslide Extraction Using Mask R-CNN with Background-Enhancement Method2022

    • Author(s)
      Yang Ruilin、Zhang Feng、Xia Junshi、Wu Chuyi
    • Journal Title

      Remote Sensing

      Volume: 14 Issue: 9 Pages: 2206-2206

    • DOI

      10.3390/rs14092206

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] DML: Differ-Modality Learning for Building Semantic Segmentation2022

    • Author(s)
      Xia Junshi、Yokoya Naoto、Baier Gerald
    • Journal Title

      IEEE Transactions on Geoscience and Remote Sensing

      Volume: 60 Pages: 1-14

    • DOI

      10.1109/tgrs.2022.3148383

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Flood Detection Using Multiple Chinese Satellite Datasets during 2020 China Summer Floods2021

    • Author(s)
      Zhang Lianchong、Xia Junshi
    • Journal Title

      Remote Sensing

      Volume: 14 Issue: 1 Pages: 51-51

    • DOI

      10.3390/rs14010051

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets2021

    • Author(s)
      Wu Chuyi、Zhang Feng、Xia Junshi、Xu Yichen、Li Guoqing、Xie Jibo、Du Zhenhong、Liu Renyi
    • Journal Title

      Remote Sensing

      Volume: 13 Issue: 5 Pages: 905-905

    • DOI

      10.3390/rs13050905

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Learning from multimodal and multitemporal earth observation data for building damage mapping2021

    • Author(s)
      Adriano Bruno、Yokoya Naoto、Xia Junshi、Miura Hiroyuki、Liu Wen、Matsuoka Masashi、Koshimura Shunichi
    • Journal Title

      ISPRS Journal of Photogrammetry and Remote Sensing

      Volume: 175 Pages: 132-143

    • DOI

      10.1016/j.isprsjprs.2021.02.016

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia2019

    • Author(s)
      Adriano Bruno、Xia Junshi、Baier Gerald、Yokoya Naoto、Koshimura Shunichi
    • Journal Title

      Remote Sensing

      Volume: 11 Issue: 7 Pages: 886-886

    • DOI

      10.3390/rs11070886

    • Related Report
      2019 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Presentation] Building Damage Mapping with Self-Positive-Unlabeled Learning2021

    • Author(s)
      Xia Junshi、Yokoya Naoto, Bruno Adriano
    • Organizer
      Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop, NeurIPS 2021
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Cross-Domain-Classification of Tsunami Damage Via Data Simulation and Residual-Network-Derived Features From Multi-Source Images2019

    • Author(s)
      Bruno Adriano ; Naoto Yokoya ; Junshi Xia ; Gerald Baier ; Shunichi Koshimura
    • Organizer
      2019 IEEE International Geoscience and Remote Sensing Symposium
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research / Invited
  • [Presentation] Building Damage Mapping Via Transfer Learning2019

    • Author(s)
      Junshi Xia ; Bruno Adriano ; Gerald Baier ; Naoto Yokoya
    • Organizer
      2019 IEEE International Geoscience and Remote Sensing Symposium
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research / Invited
  • [Funded Workshop] 2020 Conference on Computer Vision and Pattern Recognition2020

    • Related Report
      2020 Research-status Report
  • [Funded Workshop] 2019 IEEE International Geoscience and Remote Sensing Symposium2019

    • Related Report
      2019 Research-status Report
  • [Funded Workshop] 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing Symposium2019

    • Related Report
      2019 Research-status Report

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Published: 2019-04-18   Modified: 2023-01-30  

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