  Satellite imagery plays a crucial role in monitoring changes happening on
Earth's surface and aiding in climate analysis, ecosystem assessment, and
disaster response. In this paper, we tackle semantic change detection with
satellite image time series (SITS-SCD) which encompasses both change detection
and semantic segmentation tasks. We propose a new architecture that improves
over the state of the art, scales better with the number of parameters, and
leverages long-term temporal information. However, for practical use cases,
models need to adapt to spatial and temporal shifts, which remains a challenge.
We investigate the impact of temporal and spatial shifts separately on global,
multi-year SITS datasets using DynamicEarthNet and MUDS. We show that the
spatial domain shift represents the most complex setting and that the impact of
temporal shift on performance is more pronounced on change detection than on
semantic segmentation, highlighting that it is a specific issue deserving
further attention.
