Automated supervised classification of Ouagadougou built-up areas in Landsat scenes using OpenStreetMap

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    Résumé

    The ongoing development of open data policies for satellite imagery leads to new opportunities in the urban remote sensing field, such as global mapping or near-real-time monitoring. However, supervised classification that has been proved to be one of the most efficient methods to extract built-up information, happens to be inapplicable in such contexts, since the training data collection step is difficult to automate. This study explores the use of another open data project, OpenStreetMap, to collect built-up training data. In the context of Ouagadougou (Burkina Faso), we investigate the most relevant features to use and the optimal pre-processing procedures to consider. Experimental results show that we can expect similar accuracies with OSM-based training data than with the hand-digitalized ones, provided that the necessary pre-processing operations are carried out. © 2017 IEEE.
    langue originaleAnglais
    titre2017 Joint Urban Remote Sensing Event, JURSE 2017
    EditeurInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronique)9781509058082
    ISBN (imprimé)9781509058082
    Les DOIs
    Etat de la publicationPublié - 10 mai 2017
    Evénement2017 Joint Urban Remote Sensing Event, JURSE 2017 - Dubai, Émirats arabes unis
    Durée: 6 mars 20178 mars 2017

    Série de publications

    Nom2017 Joint Urban Remote Sensing Event, JURSE 2017

    Une conférence

    Une conférence2017 Joint Urban Remote Sensing Event, JURSE 2017
    PaysÉmirats arabes unis
    La villeDubai
    période6/03/178/03/17

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  • Contient cette citation

    Forget, Y., Linard, C., & Gilbert, M. (2017). Automated supervised classification of Ouagadougou built-up areas in Landsat scenes using OpenStreetMap. Dans 2017 Joint Urban Remote Sensing Event, JURSE 2017 [7924571] (2017 Joint Urban Remote Sensing Event, JURSE 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/JURSE.2017.7924571