TY - GEN
T1 - Automated supervised classification of Ouagadougou built-up areas in Landsat scenes using OpenStreetMap
AU - Forget, Yann
AU - Linard, Catherine
AU - Gilbert, Marius
PY - 2017/5/10
Y1 - 2017/5/10
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85020203470&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/automated-supervised-classification-ouagadougou-builtup-areas-landsat-scenes-using-openstreetmap
U2 - 10.1109/jurse.2017.7924571
DO - 10.1109/jurse.2017.7924571
M3 - Conference contribution
AN - SCOPUS:85020203470
SN - 9781509058082
T3 - 2017 Joint Urban Remote Sensing Event, JURSE 2017
BT - 2017 Joint Urban Remote Sensing Event, JURSE 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Joint Urban Remote Sensing Event, JURSE 2017
Y2 - 6 March 2017 through 8 March 2017
ER -