Paper Info
Title
Domain Adaptation For Semantic Segmentation Of Historical Panchromatic Orthomosaics In Central Africa
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.
Year
DOI
Venue
2021
10.3390/ijgi10080523
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Keywords
DocType
Volume
unsupervised domain adaptation, adversarial learning, correlation alignment, historical panchromatic orthomosaics, land-cover mapping, fully convolutional networks
Journal
10
Issue
Citations 
PageRank 
8
0
0.34
References 
Authors
0
11
Name
Order
Citations
PageRank
Nicholus Mboga100.34
Stefano D'Aronco200.68
T. Grippa3315.70
Charlotte Pelletier4665.97
Stefanos Georganos5224.03
Sabine Vanhuysse6317.72
Eléonore Wolff7517.90
Benoît Smets800.34
Olivier Dewitte900.34
Moritz Lennert10316.37
Jan D. Wegner1119918.05