UniVecMapper: A universal model for thematic and multi-class vector graph extraction

Abstract

With the advancements of deep learning methodologies, there have been significant strides in automating vector extraction. However, existing methods are often tailored to specific classes and are susceptible to the category variability, especially in the case of line and polygon shape objects. In this study, we propose UniVecMapper, a universal model designed to extract directional topological graphs of targets from remote sensing images, regardless of their classes. Initially, UniVecMapper leverages a topology-concentrated node detector (TNCD) to identify nodes of targets and wraps local features. Subsequently, a directional graph (DiG) generator is employed to predict the adjacency matrix of the detected nodes. To facilitate the learning of the DiG generator, we introduce a strategy namely perturbed graph supervision (PGS), which dynamically generates adjacency matrix labels based on unordered detected nodes. Comprehensive experiments conducted on the Inria, Massachusetts, and GID datasets demonstrated UniVecMapper’s universal and competitive performance in thematic vector graph extraction. Further evaluations on the multi-class polygon-shaped dataset LandCover.ai verified that UniVecMapper achieved state-of-the-art (SOTA) performance and can easily extend to multi-class tasks.

Keywords

• Vector extraction
• Directional graph prediction
• Transformer
• Deep learning
• Topology optimization