arXiv AI
Jun 12

Scalable Deep Learning Framework for Global High-Resolution Land Use Reconstruction

arXiv:2606. 11793v2 Announce Type: replace-cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models.

By Amirpasha Mozaffari, Marina Casta\~no, Stefano Materia, Etienne Tourigny, Oscar Molina-Sedano, Jordi Varela-Agrelo, Dario Garcia-Gasulla, Miguel Castrillo Melguizo, Mario Acosta, Amanda Duarte
arXiv AI
Jun 11

AI4Land: Scalable Deep Learning for Global High-Resolution Land Use Reconstruction

arXiv:2606. 11793v1 Announce Type: cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models.

By Amirpasha Mozaffari, Marina Casta\~no, Stefano Materia, Etienne Tourigny, Oscar Molina-Sedano, Jordi Varela-Agrelo, Dario Garcia-Gasulla, Miguel Castrillo Melguizo, Mario Acosta, Amanda Duarte
arXiv Computer Vision
Aug 28

Temporal Sensitivity Analysis of Tessera Embeddings

The study evaluates how the length of observation windows affects the performance of Tessera embeddings for land‑use/land‑cover mapping. By freezing the encoder and recomputing embeddings from a full year down to a single day, the authors benchmark linear probes and UNet heads on LUCAS, DynamicEarthNet, and PASTIS‑R datasets. Results show that embeddings are highly task‑dependent: for phenology‑driven classes (PASTIS‑R) they outperform from‑scratch models by ~46%, while for temporally stable classes (DynamicEarthNet, LUCAS) they match only with full supervision, yet remain more label‑efficient across all datasets.

By Julia Guerrero-Viu, Alex L\'opez-Cifuentes, Ignacio P\'erez-Villar, Fabio Pacifici