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Article

Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa

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Department of Geosciences, Environment & Society, Université Libre de Bruxelles, Av Franklin Roosevelt 50, 1050 Brussels, Belgium
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EcoVision Lab, Photogrammetry and Remote Sensing, IGP, ETH Zurich, Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland
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IRISA UMR CNRS 6074, Campus de Tohannic, Université Bretagne Sud, BP 573, 56000 Vannes, France
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Department of Earth Sciences, Royal Museum for Central Africa, Leuvensesteenweg 13, 3080 Tervuren, Belgium
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Department of Geography, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
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Institute for Computational Science, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and Georg Gartner
ISPRS Int. J. Geo-Inf. 2021, 10(8), 523; https://doi.org/10.3390/ijgi10080523
Received: 31 May 2021 / Revised: 20 July 2021 / Accepted: 29 July 2021 / Published: 1 August 2021
Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evidence to inform long-term studies. To reduce the manual labelling effort by human experts and to scale to large, meaningful regions, we investigate in this study how domain adaptation techniques and deep learning can help to efficiently map land cover in Central Africa. We propose and evaluate a methodology that is based on unsupervised adaptation to reduce the cost of generating reference data for several cities and across different dates. We present the first application of domain adaptation based on fully convolutional networks for semantic segmentation of a dataset of historical panchromatic orthomosaics for land-cover generation for two focus cities Goma-Gisenyi and Bukavu. Our experimental evaluation shows that the domain adaptation methods can reach an overall accuracy between 60% and 70% for different regions. If we add a small amount of labelled data from the target domain, too, further performance gains can be achieved. View Full-Text
Keywords: unsupervised domain adaptation; adversarial learning; correlation alignment; historical panchromatic orthomosaics; land-cover mapping; fully convolutional networks unsupervised domain adaptation; adversarial learning; correlation alignment; historical panchromatic orthomosaics; land-cover mapping; fully convolutional networks
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MDPI and ACS Style

Mboga, N.; D’Aronco, S.; Grippa, T.; Pelletier, C.; Georganos, S.; Vanhuysse, S.; Wolff, E.; Smets, B.; Dewitte, O.; Lennert, M.; Wegner, J.D. Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa. ISPRS Int. J. Geo-Inf. 2021, 10, 523. https://doi.org/10.3390/ijgi10080523

AMA Style

Mboga N, D’Aronco S, Grippa T, Pelletier C, Georganos S, Vanhuysse S, Wolff E, Smets B, Dewitte O, Lennert M, Wegner JD. Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa. ISPRS International Journal of Geo-Information. 2021; 10(8):523. https://doi.org/10.3390/ijgi10080523

Chicago/Turabian Style

Mboga, Nicholus, Stefano D’Aronco, Tais Grippa, Charlotte Pelletier, Stefanos Georganos, Sabine Vanhuysse, Eléonore Wolff, Benoît Smets, Olivier Dewitte, Moritz Lennert, and Jan Dirk Wegner. 2021. "Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa" ISPRS International Journal of Geo-Information 10, no. 8: 523. https://doi.org/10.3390/ijgi10080523

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