A radar-based method for detecting tsunami devastated areas using machine learning algorithm

Hideomi Gokon, Joachim Post, Enrico Stein, Sandro Martinis, André Twele, Matthias Mück, Shunichi Koshimura

    研究成果: Conference contribution

    抄録

    After catastrophic earthquakes and subsequent tsunamis, relief activity and reconstruction activity might be delayed due to the breakdown of information network and interception of roads to the devastated zones. To rapidly estimate the impact of the tsunami, air- or spaceborne remote sensing technologies can be used. In particular, Synthetic Aperture Radar (SAR) which is available independent of atmospheric conditions is promising. In this study, a semi-automatic method using high-resolution multi-temporal SAR data (TerraSAR-X) is proposed to estimate building damage in tsunami devastated areas related to the 2011 Tohoku earthquake tsunami. To develop the method, machine learning, a research field of artificial intelligence, is applied. Finally, evaluation of the model is conducted through cross-validation. The best accuracy is obtained as 89.2 % and kappa statistic is calculated as 0.76 when a decision tree approach (C4.5) is applied.

    本文言語English
    ホスト出版物のタイトル34th Asian Conference on Remote Sensing 2013, ACRS 2013
    出版社Asian Association on Remote Sensing
    ページ3535-3541
    ページ数7
    ISBN(印刷版)9781629939100
    出版ステータスPublished - 2013 1月 1
    イベント34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali, Indonesia
    継続期間: 2013 10月 202013 10月 24

    出版物シリーズ

    名前34th Asian Conference on Remote Sensing 2013, ACRS 2013
    4

    Other

    Other34th Asian Conference on Remote Sensing 2013, ACRS 2013
    国/地域Indonesia
    CityBali
    Period13/10/2013/10/24

    ASJC Scopus subject areas

    • コンピュータ ネットワークおよび通信

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