Progress in Earth system science is accelerating rapidly, due to the increasing availability of
multivariate datasets, often global, with moderate to high spatio-temporal resolutions …

We investigate the utility of in-domain self-supervised pretraining of vision models in the
analysis of remote sensing imagery. Self-supervised learning (SSL) has emerged as a …

Self-supervised learning (SSL) has emerged as a promising approach for remote sensing
image classification due to its ability to leverage large amounts of unlabeled data. In contrast …

Semantic segmentation of remote sensing imagery stands as a fundamental task within the
domains of both remote sensing and computer vision. Its objective is to generate a …

Satellite imagery plays a crucial role in land cover classification for various applications,
including urban planning, environmental monitoring, and land resource management. This …

Discovering new archaeological sites is key to better understand history and to protect our
heritage. Manual assessment of large amounts of remote sensing data for the detection of …