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Article Dans Une Revue ISPRS International Journal of Geo-Information Année : 2021

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

Nicholus Mboga
  • Fonction : Auteur
Stefano D’aronco
  • Fonction : Auteur
Tais Grippa
  • Fonction : Auteur
Stefanos Georganos
  • Fonction : Auteur
Sabine Vanhuysse
  • Fonction : Auteur
Eléonore Wolff
  • Fonction : Auteur
Benoît Smets
  • Fonction : Auteur
Olivier Dewitte
  • Fonction : Auteur
Moritz Lennert
  • Fonction : Auteur
Jan Dirk Wegner
  • Fonction : Auteur

Résumé

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.
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Dates et versions

hal-03356226 , version 1 (25-10-2021)

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Nicholus Mboga, Stefano D’aronco, Tais Grippa, Charlotte Pelletier, Stefanos Georganos, et al.. Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa. ISPRS International Journal of Geo-Information, 2021, 10 (8), pp.523. ⟨10.3390/ijgi10080523⟩. ⟨hal-03356226⟩
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