GeoVLM-R1: Reinforcement Fine-Tuning for Improved Remote Sensing Reasoning
This work addresses the problem of improving reasoning capabilities for remote sensing images, which is incremental as it builds on existing RL methods for a specific domain.
The paper tackles the challenge of adapting reinforcement learning models to Earth Observation tasks by proposing a post-training framework with task-aware rewards, resulting in consistent performance gains across multiple benchmarks.
Recent advances in reinforcement learning (RL) have delivered strong reasoning capabilities in natural image domains, yet their potential for Earth Observation (EO) remains largely unexplored. EO tasks introduce unique challenges, spanning referred object detection, image or region captioning, change detection, grounding, and temporal analysis, that demand task aware reasoning. We propose a novel post training framework that incorporates task aware rewards to enable effective adaptation of reasoning based RL models to diverse EO tasks. This training strategy enhances reasoning capabilities for remote sensing images, stabilizes optimization, and improves robustness. Extensive experiments across multiple EO benchmarks show consistent performance gains over state of the art generic and specialized vision language models. Code and models will be released publicly at https://mustansarfiaz.github.io/GeoVLM-R1/ .