Street2GIS: Multimodal Generative Framework for Pedestrian Infrastructure Mapping

The increasing demand for smart urban systems and advanced mobility solutions highlights the critical need for precise, multimodal, and up-to-date Geographic Information System (GIS) data. While vehicular networks are well-mapped, pedestrian infrastructure often remains underrepresented, posing challenges for autonomous navigation and urban analysis. To address this issue, we present Street2GIS, an end-to-end open-source, multimodal framework that integrates multiview and cross-cue data fusion for automating the generation of GIS shapefiles for pedestrian infrastructure from street-view imagery and geo-coordinate data. Street2GIS employs hierarchical transformation networks to combine depth maps, semantic maps, and elevation data for geospatial feature extraction and the creation of georeferenced shapefiles. The results accurately identify roads, sidewalks, buildings, and vegetation, ensuring precise spatial alignment of urban elements within a 70-meter radius of each location. Evaluated using ground truth data from the Estonian Topographic Database, the framework demonstrated robust performance in Tartu, Estonia, achieving an F1-score exceeding 90% and alignment and similarity metrics of 91% and 72.95%, respectively. The proposed framework efficiently produces detailed urban maps and provides timely, high-resolution pedestrian infrastructure data suitable for applications such as autonomous navigation, urban planning, and environmental monitoring.