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

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

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.
langueAnglais
titre2017 Joint Urban Remote Sensing Event, JURSE 2017
EditeurInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronique)9781509058082
ISBN (imprimé)9781509058082
Les DOIs
étatPublié - 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

Empreinte digitale

image classification
Landsat
education
preprocessing
Satellite imagery
Burkina
Processing
Remote sensing
satellite imagery
Burkina Faso
Monitoring
remote sensing
monitoring
built-up area

Citer ceci

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
Forget, Yann ; Linard, Catherine ; Gilbert, Marius. / Automated supervised classification of Ouagadougou built-up areas in Landsat scenes using OpenStreetMap. 2017 Joint Urban Remote Sensing Event, JURSE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 Joint Urban Remote Sensing Event, JURSE 2017).
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title = "Automated supervised classification of Ouagadougou built-up areas in Landsat scenes using OpenStreetMap",
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. {\circledC} 2017 IEEE.",
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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., 2017 Joint Urban Remote Sensing Event, JURSE 2017, Dubai, Émirats arabes unis, 6/03/17. https://doi.org/10.1109/JURSE.2017.7924571

Automated supervised classification of Ouagadougou built-up areas in Landsat scenes using OpenStreetMap. / Forget, Yann; Linard, Catherine; Gilbert, Marius.

2017 Joint Urban Remote Sensing Event, JURSE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7924571 (2017 Joint Urban Remote Sensing Event, JURSE 2017).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

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AB - 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.

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