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

    Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

    Abstract

    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.
    Original languageEnglish
    Title of host publication2017 Joint Urban Remote Sensing Event, JURSE 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781509058082
    ISBN (Print)9781509058082
    DOIs
    Publication statusPublished - 10 May 2017
    Event2017 Joint Urban Remote Sensing Event, JURSE 2017 - Dubai, United Arab Emirates
    Duration: 6 Mar 20178 Mar 2017

    Publication series

    Name2017 Joint Urban Remote Sensing Event, JURSE 2017

    Conference

    Conference2017 Joint Urban Remote Sensing Event, JURSE 2017
    CountryUnited Arab Emirates
    CityDubai
    Period6/03/178/03/17

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