GeoRL: Adaptive Tokenization Via Reinforcement Learning for Remote Sensing Foundation Models

Vision Foundation Models (VFMs) have had success in transferring learned visual representations from discrete tasks to other tasks. However, VFMs for interpreting remote sensing images are limited due to heterogeneous properties found in Earth observation data: spatial scale, semantic complexity, and geographical context. To address this issue, we introduce GeoRL, an Adaptive Visual Tokenization Framework that treats token allocation as an MDP and allows VFMs dynamic token allocation decisions based on information density of a region. We developed a lightweight Policy Network trained with Proximal Policy Optimization (PPO) that learns to maximize the reward function composition of tokenization performance and computational efficiency. We also propose Hierarchical Semantic Anchoring (HSA) for interpreting tokenization policies learned. By performing extensive experiments on four established benchmark datasets (DOTA, iSAID, LoveDA, and xView), we have established that GeoRL outperforms all other competitors across three tasks of scene classification, semantic segmentation, and object detection, while also achieving a reduction in computational cost of 47%-63% when compared with uniform tokenization techniques. The policy learned by GeoRL can be used directly with other sources of imagery (e.g., SAR, multispectral) without requiring retraining, indicating that GeoRL leverages the spatial reasoning patterns inherent to all remote sensing imagery regardless of the modality. Theoretically, GeoRL has proof of convergence guarantees with mild assumptions and provides insight into the sample complexity associated with learning a policy in the remote sensing domain.