LULC Segmentation in Historical Images Under Domain Shift: An Empirical Study

Agricultural abandonment is a global trend leading to vegetation succession and Forests expansion. Manual annotations of 1946 and 2019 aerial surveys images from a peri-urban area in Massif Central shows Land Use and Land Cover (LULC) evolution in this period. We propose to use a convolutional neural network trained on labelled years images to predict LULC maps from 13 intermediate years unlabelled images. However, sensors variety used for acquisition during this time induce variability in ground sampling distances and colorimetry. We have shown using transfer between labelled years that sampling distances have to be the same in the training and testing set, and that coarse scaling offer sufficient performances for the considered LULC classes. Colorimetric data augmentations were individually used after sampling unification to make models more robust to sensors and illumination changes, but proved to be inconsistent in transfer on intermediate years.