More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe
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 achieve competitive or state-of-the-art performance at challenging remote sensing benchmarks, provided that it is trained at sufficient scale across diverse data and tasks. Our model uses a single language policy that can ei...
| Authors | Stefan Maria Ailuro, Mario Markov, Mohammad Mahdi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 July 2026 |
| Last updated at source | 1 October 2026 |
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