OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery

While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to billions of parameters has been shown to yield unprecedented benefits including emergent abilities, but requires data scali...

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Podrobná bibliografia

Vydané v:	arXiv.org
Hlavní autori:	Dias, Philipe, Tsaris, Aristeidis, Bowman, Jordan, Potnis, Abhishek, Arndt, Jacob, Yang, H Lexie, Dalton Lunga
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Ithaca Cornell University Library, arXiv.org 25.10.2024
Predmet:	Best practice Computation High resolution Image classification Image resolution Image segmentation Object recognition Parameters Remote sensing Satellite imagery Scaling Semantic segmentation
ISSN:	2331-8422
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