Vector Map as Language: Toward Unified Remote Sensing Vector Mapping

2026-06-09

AI Summary

This work addresses the challenge of unified modeling for multi-class heterogeneous geographic entities in remote sensing vector mapping, where existing methods struggle to adequately represent topological relationships and instance boundaries. The authors propose reframing vector map construction as a structured text generation task by designing a GeoJSON-like hierarchical vector language that jointly encodes geometric, semantic, and topological information. A progressive vision-to-language mapping framework is introduced, optimized via reinforcement learning to ensure syntactic validity, content fidelity, and map executability of the generated output, thereby enabling cross-category unified modeling. Experiments on the newly curated VecMap-Bench dataset—comprising 54K images and 800K instances—demonstrate that the proposed approach significantly outperforms state-of-the-art methods in single- and multi-class mapping, cross-dataset transfer, and open-vocabulary generalization.

Abstract

Remote sensing vector mapping aims to generate structured maps of geospatial entities, such as buildings, roads, and water bodies, from remote sensing imagery. In practice, vector maps usually contain multiple category layers and heterogeneous entity structures, requiring a unified model for diverse mapping needs. However, existing methods typically represent vector objects as polygons or graphs, making them suitable only for specific categories: polygons poorly capture topological relations, while graphs often blur instance boundaries. We observe that language, as a natural medium for human communication, offers a flexible and expressive representation that can accommodate heterogeneous map elements, including geometry, semantics, and topolog. Motivated by this insight, we propose Vector Map as Language (VecLang), a unified paradigm that reformulates multiclass vector mapping as structured text generation. VecLang encodes the common elements of different geospatial entities into a GeoJSON-like vector language, enabling cross-category modeling within a shared textual format. To generate this language reliably, we design a progressive vision-language mapping framework that first localizes vectorization units and then generates structured map elements. We further introduce Hierarchical Vector Language Optimization, which uses reinforcement learning to improve syntax validity, content fidelity, and map executability. We also build VecMap-Bench with 54K images and 800K instances, supporting training and evaluation across standard and generalization settings. Extensive experiments demonstrate that VecLang handles both single-class and multiclass vector mapping while achieving strong cross-dataset and open-vocabulary generalization. The model and dataset are publicly available at https://github.com/yyyyll0ss/VecLang.

Problem

• remote sensing vector mapping
• unified modeling
• heterogeneous entity structures
• topological relations
• instance boundaries

Innovation

• vector mapping
• structured text generation
• vision-language framework
• reinforcement learning optimization
• cross-category generalization