The following repo contains scripts used in the deep learning analysis of remote sensing data provided by the Royal Museum For Central Africa in the PASTECA project. The publications below were as a result of the developed scripts.
Fully convolutional networks for land cover classification from historical panchromatic aerial photographs Mboga, N.O., Grippa, T., Georganos, S., Vanhuysse, S., Smets, B., Dewitte, O., Wolff,E., & Lennert, M ISPRS journal of photogrammetry and remote sensing 2020
Domain adaptation for semantic segmentation of historical panchromatic orthomosaics in Central Africa Mboga, N.O., D’Aronco, S., Grippa, T., Pelletier, C., Georganos, S.,Vanhuysse, S., Wolff, E., Smets, B., Dewitte, O, Lennert, M., and Wegner, JD ISPRS International Journal of Geo-Information 2021
Historical dynamics of landslide risk from population and forest-cover changes in the Kivu Rift Depicker, A., Jacobs, L., Mboga, N.O., Smets, B., Van Rompaey, A., Lennert, M., Wolff, E., Kervyn, F., Michellier, C., Dewitte, O. and Govers, G Nature Sustainability 2021