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

Authors :
Nicholus Mboga
Stefano D’Aronco
Tais Grippa
Charlotte Pelletier
Stefanos Georganos
Sabine Vanhuysse
Eléonore Wolff
Benoît Smets
Olivier Dewitte
Moritz Lennert
Jan Dirk Wegner
Source :
ISPRS International Journal of Geo-Information, Vol 10, Iss 8, p 523 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
22209964
Volume :
10
Issue :
8
Database :
Directory of Open Access Journals
Journal :
ISPRS International Journal of Geo-Information
Publication Type :
Academic Journal
Accession number :
edsdoj.85681d387c66413c9526869838b32321
Document Type :
article
Full Text :
https://doi.org/10.3390/ijgi10080523