SVI2POI: an end-to-end framework for POI generation from street-view imagery

Points of Interest (POIs) data are vital for location-based services, yet their production remains challenging due to labor-intensive collection and verification processes. Generating POIs from street-view imagery (SVI) has recently emerged as a promising solution. However, the lack of open benchmark hinders its development. Existing methods typically treat SVI as isolated images without fully leverage their multi-view representations of geographical entities. We present SVI2POI, a novel end-to-end framework for POI extraction from SVI. It brings two key innovations. In the signboard recognition stage, the proposed YOLOv11s-DLKA detector enhanced performance degraded by geometric distortions commonly occurred in SVI. In the POI generation stage, we propose a clustering strategy combined with large language model-based naming and photogrammetric positioning to consolidate multi-view information for accurate POI identification. Furthermore, we introduce the first open dataset for end-to-end POI generation from SVI. It contains a training dataset including 3,097 SVIs with 13,182 manually annotated regions of interest (ROIs), and a benchmark dataset with 927 SVIs and 1,004 manually labeled POIs, with 190 verified against OpenStreetMap-POIs and therefore contains coordinates. Our framework achieves 61.69% precision, 50.70% recall, and 55.66% F1-score, outperforms state-of-art method with 5.59%, 1.16%, and 3.04%, respectively, via cross-method and cross-dataset comparison.