DM-KG: A Novel Method for Boosting Spatial Cognition of Vision-Language Models in Street View Imagery

• URL: http://arxiv.org/abs/2607.12319v1
• Date: Tue, 14 Jul 2026 03:52:37 GMT
• Title: DM-KG: A Novel Method for Boosting Spatial Cognition of Vision-Language Models in Street View Imagery
• Abstract summary: This study proposes DM-KG (Direction-Metric Knowledge Graph), a grounded spatial representation framework for street view imagery.<n>By explicitly extracting directional and metric relationships between entities from a single 2D image, this framework enhances spatial reasoning accuracy.<n> Experimental results demonstrate that DM-KG reduces the mean absolute error (MAE) in distance estimation by 31.1% and the mean angular error in direction judgment by 65.8%.
• Score: 29.356253148114135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As vision-language models (VLMs) are increasingly deployed in geospatial question answering and visual scene understanding, improving their spatial cognition capability on street view imagery for complex logical reasoning has emerged as a key research priority. However, existing VLMs frequently suffer from "spatial semantic hallucinations" when perceiving object locations, distances, and directions in real-world street view scenes. Furthermore, such errors are often recalcitrant to tracing and calibration, posing a critical bottleneck for their practical deployment in geospatial tasks. To address this pressing challenge, this study proposes DM-KG (Direction-Metric Knowledge Graph), a structurally grounded spatial representation framework for street view imagery. By explicitly extracting directional and metric relationships between entities from a single 2D image, this framework enhances the spatial reasoning accuracy of VLMs through a structured knowledge graph. Specifically, we integrate panoptic segmentation with metric depth estimation to robustly compute entity-level 3D spatial coordinates. Subsequently, we encode the clock azimuths and Euclidean distances of entity pairs into a JSON-formatted knowledge graph, which is injected into the VLM as an explicit geometric prior to guide spatial reasoning. Experimental results on public spatial question-answering (QA) benchmarks demonstrate that DM-KG reduces the mean absolute error (MAE) in distance estimation by 31.1% and the mean angular error in direction judgment by 65.8%, while simultaneously maintaining a high QA success rate. By establishing a complete, augmented reasoning pipeline, this research significantly improves the spatial cognitive capabilities of VLMs in street view scenarios, thereby providing a flexible, generalized, and interpretable framework for geographic visual question answering (GeoVQA) in open environments.

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