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

Temporal Sensitivity Analysis of Tessera Embeddings

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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