Abstract

The accelerated pace of urbanization across the Global South calls for precise, real-time building footprint data to underpin effective urban governance and enhance disaster resilience. Conventional mapping approaches, however, suffer from inefficiency in data acquisition and updating. Although Volunteered Geographic Information (VGI) provides a crowdsourced solution for geospatial data collection, it is commonly hindered by significant heterogeneity—manifested in inconsistent data completeness, positional inaccuracies and poor topological consistency across different datasets. To address these critical limitations, this study proposes an intelligent geospatial agent framework designed to autonomously fuse building data from multiple heterogeneous sources, including VGI, Very High-Resolution (VHR) satellite imagery, and Light Detection and Ranging (LiDAR) data. This study’s core innovative points are embodied in three key modules: a supervised VGI quality verification module that leverages the Random Forest model to evaluate the reliability of individual building feature elements; a hybrid building extraction engine which integrates LiDAR data with the Segment Anything Model (SAM) to realize zero-shot building extraction; and a cognitive rule engine that adopts Multi-Criteria Decision Analysis (MCDA) for the intelligent resolution of spatial conflicts. Comprehensive validation experiments were conducted in two African cities experiencing rapid urbanization—Kigali and Dar es Salaam. The results show that the proposed framework boosts data completeness by more than 29% and attains a fused dataset F1-Score of 0.919, effectively converting incomplete VGI data into a geospatial resource with near-official authoritative quality.

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