Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models

• URL: http://arxiv.org/abs/2502.09356v1
• Date: Thu, 13 Feb 2025 14:21:03 GMT
• Title: Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models
• Authors: Gabriel Tseng, Anthony Fuller, Marlena Reil, Henry Herzog, Patrick Beukema, Favyen Bastani, James R. Green, Evan Shelhamer, Hannah Kerner, David Rolnick,
• Abstract summary: We introduce a novel and highly effective self-supervised learning approach to learn both large- and small-scale features. Our Galileo models obtain state-of-the-art results across diverse remote sensing tasks.
• Score: 34.71460539414284
• License:
• Abstract: From crop mapping to flood detection, machine learning in remote sensing has a wide range of societally beneficial applications. The commonalities between remote sensing data in these applications present an opportunity for pretrained machine learning models tailored to remote sensing to reduce the labeled data and effort required to solve individual tasks. However, such models must be: (i) flexible enough to ingest input data of varying sensor modalities and shapes (i.e., of varying spatial and temporal dimensions), and (ii) able to model Earth surface phenomena of varying scales and types. To solve this gap, we present Galileo, a family of pretrained remote sensing models designed to flexibly process multimodal remote sensing data. We also introduce a novel and highly effective self-supervised learning approach to learn both large- and small-scale features, a challenge not addressed by previous models. Our Galileo models obtain state-of-the-art results across diverse remote sensing tasks.

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