Cross-Domain-Classification of Tsunami Damage Via Data Simulation and Residual-Network-Derived Features from Multi-Source Images

Bruno Adriano, Naoto Yokoya, Junshi Xia, Gerald Baier, Shunichi Koshimura

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    This paper presents a novel application of remote sensing data and machine learning technologies for damage classification in a real-world cross-domain application. The proposed methodology trains models to learn the building damage characteristics recorded in the 2011 Tohoku Tsunami from multi-sensor and multi-temporal remote sensing images. Then, the trained models are tested in the recent 2018 Sulawesi Tsunami. Additionally, a simulation of high-resolution SAR image was carried to deal with missing data modality. Our initial results show that the ResNet-derived features from optical images acquired after the disaster together with moderate- and high-resolution synthetic aperture radar (SAR) post-event intensity data showed significant accuracy in classifying two levels of tsunami-induced damage, with an average f-score of approximately 0.72. Taking into account that no training data from the 2018 Sulawesi Tsunami was used, our methodology shows excellent potential for future implementation of a rapid response system based on a database of building damage constructed from previous majors disasters.

    Original languageEnglish
    Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages4947-4950
    Number of pages4
    ISBN (Electronic)9781538691540
    DOIs
    Publication statusPublished - 2019 Jul
    Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
    Duration: 2019 Jul 282019 Aug 2

    Publication series

    NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

    Conference

    Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
    Country/TerritoryJapan
    CityYokohama
    Period19/7/2819/8/2

    Keywords

    • Conditional generative adversarial network
    • cross-domain classification
    • residual networks
    • tsunami-induced damage.

    ASJC Scopus subject areas

    • Computer Science Applications
    • Earth and Planetary Sciences(all)

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