TopoSense: agent driven topological graph extraction from remote sensing image

Abstract

Abstract. Automatic topological graph extraction is critical for intelligent remote sensing image interpretation and cartographic representation. However, existing approaches neither adopt segmentation-based post-processing nor directly predict the graph, thereby suffering from limited scalability and poor adaptability to complex spatial structures. To address these issues, we introduce TopoSense, an innovative framework for extracting topological graphs from remote sensing images through an agent-driven approach. By employing a novel combination of reinforcement learning and neural network architectures, TopoSense autonomously navigates through pixel-level data, efficiently constructing topological representations. It not only enhances the accuracy of spatial feature detection, but also significantly reduces processing time. Experiments on the TOP-BOUNDARY and REALSCENE demonstrate its superiority in capturing intricate spatial relationships compared to traditional methods.