  Modern Earth Observation systems provide sensing data at different temporal
and spatial resolutions. Among optical sensors, today the Sentinel-2 program
supplies high-resolution temporal (every 5 days) and high spatial resolution
(10m) images that can be useful to monitor land cover dynamics. On the other
hand, Very High Spatial Resolution images (VHSR) are still an essential tool to
figure out land cover mapping characterized by fine spatial patterns.
Understand how to efficiently leverage these complementary sources of
information together to deal with land cover mapping is still challenging. With
the aim to tackle land cover mapping through the fusion of multi-temporal High
Spatial Resolution and Very High Spatial Resolution satellite images, we
propose an End-to-End Deep Learning framework, named M3Fusion, able to leverage
simultaneously the temporal knowledge contained in time series data as well as
the fine spatial information available in VHSR information. Experiments carried
out on the Reunion Island study area asses the quality of our proposal
considering both quantitative and qualitative aspects.
