Disaster detection from aerial imagery with convolutional neural network

Siti Nor Khuzaimah Binti Amit, Yoshimitsu Aoki

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

    12 Citations (Scopus)

    Abstract

    In recent years, analysis of remote sensing imagery is imperatives in the domain of environmental and climate monitoring primarily for the application of detecting and managing a natural disaster. Satellite imagery or aerial imagery is beneficial because it can widely capture the condition of the surface ground and provides a massive amount of information in a piece of satellite imagery. Since obtaining satellite imagery or aerial imagery is getting more ease in recent years, landslide detection and flood detection is highly in demand. In this paper, we propose automatic natural disaster detection particularly for landslide and flood detection by implementing convolutional neural network (CNN) in extracting the feature of disaster more effectively. CNN is robust to shadow, able to obtain the characteristic of disaster adequately and most importantly able to overcome misdetection or misjudgment by operators, which will affect the effectiveness of disaster relief. The neural network consists of 2 phases: Training phase and testing phase. We created training data patches of pre-disaster and post-disaster by clipping and resizing aerial imagery obtained from Google Earth Aerial Imagery. We are currently focusing on two countries which are Japan and Thailand. Training dataset for both landslide and flood consist of 50000 patches. All patches are trained in CNN to extract region where changes occurred or known as disaster region occurred without delay. We obtained accuracy of our system in around 80%-90% of both disaster detections. Based on the promising results, the proposed method may assist in our understanding of the role of deep learning in disaster detection.

    Original languageEnglish
    Title of host publicationProceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages239-245
    Number of pages7
    Volume2017-January
    ISBN (Electronic)9781538607169
    DOIs
    Publication statusPublished - 2017 Dec 19
    Event6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 - Surabaya, Indonesia
    Duration: 2017 Sep 262017 Sep 27

    Other

    Other6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
    CountryIndonesia
    CitySurabaya
    Period17/9/2617/9/27

    Keywords

    • aerial imagery
    • change detection
    • disaster detection
    • flood convolutional neural network
    • landslide

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Signal Processing

    Fingerprint Dive into the research topics of 'Disaster detection from aerial imagery with convolutional neural network'. Together they form a unique fingerprint.

  • Cite this

    Amit, S. N. K. B., & Aoki, Y. (2017). Disaster detection from aerial imagery with convolutional neural network. In Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 (Vol. 2017-January, pp. 239-245). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/KCIC.2017.8228593