MURAL - Maynooth University Research Archive Library

    Automatic detection of passable roads after floods in remote sensed and social media data

    Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Ostroukhova, Olga and Halvorsen, Pål and Conci, Nicola and Dahyot, Rozenn (2019) Automatic detection of passable roads after floods in remote sensed and social media data. Signal Processing: Image Communication, 74. pp. 110-118. ISSN 0923-5965

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    This paper addresses the problem of floods classification and floods aftermath detection based on both social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods is carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches.

    Item Type: Article
    Keywords: Flood detection; Convolutional neural networks; Natural disasters; Social media; Satellite imagery; Multimedia indexing and retrieval;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15100
    Identification Number:
    Depositing User: Rozenn Dahyot
    Date Deposited: 07 Dec 2021 15:58
    Journal or Publication Title: Signal Processing: Image Communication
    Publisher: Elsevier
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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