Smoothing Continual Segmentation Oscillations with Latent Domain PPCA Decoder

We study Domain Incremental Learning for the semantic segmentation of Earth Observation images. We demonstrate that controlling the oscillation of performance when a new domain arrives is more critical than controlling catastrophic forgetting. We propose an exemplar-free architecture that combines a large pre-trained network well adapted to dense image processing (DINOv2) and a generative decoder head based on Probabilitic Principal Component Analysis (PPCA). We validate our approach on the FLAIR#1 high-resolution dataset, which is structured as a sequence of domains.