Title: Geo-R1: Unlocking VLM Geospatial Reasoning with Cross-View Reinforcement Learning
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Reinforcement Learning, Vision Language Model, Geospatial Reasoning

Abstract: We introduce Geo-R1, a reasoning-centric post-training framework that unlocks geospatial reasoning in vision-language models by combining thinking scaffolding and elevating. In the scaffolding stage, Geo-R1 instills a ``geospatial thinking paradigm" via supervised fine-tuning on synthetic chain-of-thought exemplars, enabling models to connect visual cues with geographic priors without costly human reasoning annotations. In the elevating stage, it uses GRPO-based reinforcement learning on a weakly-supervised cross-view pairing proxy. This design supplies a verifiable and scalable reward signal: teaching models to capture and reconcile features across modalities, and harnessing reasoning for accurate prediction. Geo-R1 extends geospatial modeling from domain pretraining / supervised finetuning to reasoning-first post-training, and achieves state-of-the-art performance across various geospatial reasoning benchmarks. Our code is available at https://anonymous.4open.science/r/Geo-R1-ICLR.