5 papers · 1 filter
Parameter-Efficient Adaptation of Geospatial Foundation Models through Embedding Deflection
Romain Thoreau, Valerio Marsocci, Dawa Derksen
As large-scale heterogeneous data sets become increasingly available, adapting foundation models at low cost has become a key issue. Seminal works in natural language processing, e…
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?
Yuru Jia, Valerio Marsocci, Ziyang Gong +3
Self-supervised learning (SSL) has revolutionized representation learning in Remote Sensing (RS), advancing Geospatial Foundation Models (GFMs) to leverage vast unlabeled satellite…
PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
Valerio Marsocci, Yuru Jia, Georges Le Bellier +12
Geospatial Foundation Models (GFMs) have emerged as powerful tools for extracting representations from Earth observation data, but their evaluation remains inconsistent and narrow.…
Cross-sensor self-supervised training and alignment for remote sensing
Valerio Marsocci, Nicolas Audebert
Large-scale ''foundation models'' have gained traction as a way to leverage the vast amounts of unlabeled remote sensing data collected every day. However, due to the multiplicity…
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
Valerio Marsocci, Nicolas Gonthier, Anatol Garioud +2
Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (R…