Feb. 15, 2024, 5:43 a.m. | Carlos Echegoyen, Aritz P\'erez, Guzm\'an Santaf\'e, Unai P\'erez-Goya, Mar\'ia Dolores Ugarte

cs.LG updates on arXiv.org arxiv.org

arXiv:2208.13504v3 Announce Type: replace-cross
Abstract: Temporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to different factors such as the lack of precise labeled data, the definition and variability of the terrain entities, or the inherent complexity of the images and their fusion. In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal …

abstract acquisition analysis arxiv cs.cv cs.lg data images knowledge satellite satellite images scale semantic temporal type unsupervised vast

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