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An open-source semi-automated processing chain for urban obia classification

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Grippa, T. and Lennert, M. and Beaumont, B. and Vanhuysse, S. and Stephenne, N. and Wolff, E. (2016) An open-source semi-automated processing chain for urban obia classification. In: GEOBIA 2016 : Solutions and Synergies., 14 September 2016 - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC) .

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Event: GEOBIA 2016 : Solutions and Synergies., 14 September 2016 - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC)
Abstract:This study presents the development of a semi-automated processing chain for OBIA urban land-cover and land-use classification. Implemented in Python and relying on existing open-source software GRASS GIS and R. The complete tool chain is available in open-access and adaptable to specific user needs. For automation purpose, we developed two GRASS GIS add-ons allowing (1) to optimize segmentation parameters in an unsupervised manner and (2) to classify remote sensing data using several individual machine learning classifiers or their predictions combination through voting-schemes. We tested the performance and transferability of the processing chain using sub-metric multispectral and height data on two very different urban environments: Ouagadougou, Burkina Faso in sub-Saharan Africa and Liège, Belgium in Western Europe. Using a hierarchical classification scheme, the kappa values reached for both cities about 0.78 at the second level (9 and 11 classes) and 0.90 at the first level (5 classes).
Item Type:Conference or Workshop Item (Paper)
Link to this item:https://doi.org/10.3990/2.367
Conference URL:https://www.geobia2016.com/
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