Satellite imagery analysis for operational damage assessment in emergency situations

German Novikov, Alexey Trekin, Georgy Potapov, Vladimir Ignatiev, Evgeny Burnaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    23 Citations (Scopus)

    Abstract

    When major disaster occurs the questions are raised how to estimate the damage in time to support the decision making process and relief efforts by local authorities or humanitarian teams. In this paper we consider the use of Machine Learning and Computer Vision on remote sensing imagery to improve time efficiency of assessment of damaged buildings in disaster affected area. We propose a general workflow that can be useful in various disaster management applications, and demonstrate the use of the proposed workflow for the assessment of the damage caused by the wildfires in California in 2017.

    Original languageEnglish
    Title of host publicationBusiness Information Systems - 21st International Conference, BIS 2018, Proceedings
    EditorsWitold Abramowicz, Adrian Paschke
    PublisherSpringer Verlag
    Pages347-358
    Number of pages12
    ISBN (Print)9783319939308
    DOIs
    Publication statusPublished - 2018
    Event21st International Conference on Business Information Systems, BIS 2018 - Berlin, Germany
    Duration: 18 Jul 201820 Jul 2018

    Publication series

    NameLecture Notes in Business Information Processing
    Volume320
    ISSN (Print)1865-1348

    Conference

    Conference21st International Conference on Business Information Systems, BIS 2018
    Country/TerritoryGermany
    CityBerlin
    Period18/07/1820/07/18

    Keywords

    • Damage assessment
    • Deep learning
    • Emergency mapping
    • Emergency response
    • Remote sensing
    • Satellite imagery

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