Automated Quality Assessment of Geospatial Vector Data: A GeoAI Approach using Spatial Representation Learning

2026-06-23

AI Summary

This study addresses the limitations of traditional rule-based methods for geospatial vector data quality assessment, which struggle with complex urban morphologies and massive datasets while relying heavily on manual annotation. To overcome these challenges, the authors propose Topo4Vec, a novel framework that pioneers the application of spatial representation learning directly to native vector data for quality evaluation. By simulating topological errors to generate training samples and employing deep geometric encoders to map polygonal and polyline structures into a latent space, Topo4Vec automatically discriminates between valid and erroneous topologies without requiring human-labeled data. The method supports large-scale, automated detection across multiple error types. Experiments in Los Angeles, Munich, and Singapore demonstrate its robustness and scalability, achieving 0.99 accuracy in building overlap detection and 0.60 accuracy in identifying over- and under-extension errors in street networks.

Abstract

Geospatial vector data quality is a foundational research topic in GIS, yet classic rule-based quality assessment algorithms often struggle with diverse urban morphologies and massive data volumes. Recently, Geospatial Artificial Intelligence (GeoAI) shows promising potential for automating geospatial analysis, while its application to native vector data remains largely underexplored. To fill this research gap, we proposed Topo4Vec, an automated GeoAI framework, designed for scalable vector data quality assessment via advanced Spatial Representation Learning (SRL). Specifically, Topo4Vec relax the labor-intensive manual annotation process via topological error simulation, such as overlapping polygons and street network connectivity errors e.g., overshoots and undershoots. Then, it leverages state-of-the-art SRL approaches to encode complex, native vector geometries (e.g., polylines and polygons) into a latent space where topological errors are isolated from valid ones. A systematic performance evaluation across three study areas (Los Angeles, Munich, and Singapore) demonstrates the effectiveness and robustness of Topo4Vec, achieving a peak accuracy of 0.99 for detecting overlapping building footprints and 0.60 for overshoots and undershoots in street networks. Moreover, lessons learned from Topo4Vec shed a promising light into a scalable and autonomous GeoAI approach for large-scale vector data consistency and quality monitoring within the fast-growing geospatial data ecosystems. The code and data used in the paper are made openly available in https://figshare.com/s/612148eeb4bccadbd715.

Problem

• Geospatial vector data quality
• Automated quality assessment
• Topological errors
• GeoAI
• Spatial Representation Learning

Innovation

• GeoAI
• Spatial Representation Learning
• Topo4Vec
• vector data quality
• topological error detection