# Do Satellite Tasks Need Special Pretraining?

Source: [Do Satellite Tasks Need Special Pretraining?](<https://www.sota2.com/research/paper/68fa7c571e209131cea8da98>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T10:39:22.431Z

Authors: Ani Vanyan, Alvard Barseghyan, Hakob Tamazyan, Tigran Galstyan, Vahan Huroyan, Naira Hovakimyan, Hrant Khachatrian

Published: 2025-10-19T21:32:01.000Z

## Abstract

Foundation models have advanced machine learning across various modalities, including images. Recently multiple teams trained foundation models specialized for remote sensing applications. This line of research is motivated by the distinct characteristics of remote sensing imagery, specific applications and types of robustness useful for satellite image analysis. In this work we systematically challenge the idea that specific foundation models are more useful than general-purpose vision foundation models, at least in the small scale. First, we design a simple benchmark that measures generalization of remote sensing models towards images with lower resolution for two downstream tasks. Second, we train iBOT, a self-supervised vision encoder, on MillionAID, an ImageNet-scale satellite imagery dataset, with several modifications specific to remote sensing. We show that none of those pretrained models bring consistent improvements upon general-purpose baselines at the ViT-B scale.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.17014>)

[PDF](<https://arxiv.org/pdf/2510.17014.pdf>)