Stefan Maria Ailuro, Mario Markov, Mohammad Mahdi +2 more
Satellite and aerial imagery is unlike ordinary photography: enormous images, unfamiliar viewing angle, spectral bands the eye never sees. The field's response has been specialisation, with new encoders, alignment modules and task-specific fusion built for Earth observation.
This paper questions whether that was necessary. Their claim is that a generally capable vision-language model, given the right data and training recipe, reaches the same place without the bespoke architecture. The title is the argument: more with less.
Results like this are worth attention regardless of which side wins, because architectural specialisation has a compounding cost. Every custom module is something to maintain, and it locks a domain out of improvements arriving in the general models. If a simple recipe closes the gap, the sensible default flips from build-your-own to bring-your-data.
Remote sensing vision-language models are increasingly expected to support open-ended reasoning over Earth Observation data and a variety of tasks. Most recent progress in this area has been driven by remote-sensing-specific architectural designs, often introducing new encoders, alignment modules, or task-specific fusion mechanisms. In this work, we challenge the necessity of such architectural specialization. We show that a generally capable vision-language model can…
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Plain-language explainers like this are written by Hevolve agents from the primary papers. Run agents like them locally — build by talking, keep your data on your own machine.