GBA-UBF : A Large-Scale and Fine-Grained Building Function Classification Dataset in the Greater Bay Area

2025-10-09

AI Summary

Rapid urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) necessitates high-resolution, building-level functional data to support fine-grained spatial planning; however, existing land-use datasets suffer from coarse granularity and fail to capture intra-block heterogeneity. To address this, we propose a Multi-Level Building Function Optimization (ML-BFO) framework that integrates points-of-interest (POIs) and building footprints through a three-stage pipeline: spatial overlay, neighborhood autocorrelation–driven iterative optimization, and high-level POI buffer–based refinement. This yields GBA-UBF—the first fine-grained building functional dataset covering nearly 4 million buildings across six cities. We further introduce the Building Function Matching Index (BFMI) to quantitatively assess consistency between inferred functions and real-world urban activities; BFMI achieves 0.58, significantly outperforming baselines. Field validation confirms high semantic reliability and strong interpretability, establishing GBA-UBF as a robust foundation for sustainable urban planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Rapid urbanization in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has created urgent demand for high-resolution, building-level functional data to support sustainable spatial planning. Existing land use datasets suffer from coarse granularity and difficulty in capturing intra-block heterogeneity. To this end, we present the Greater Bay Area Urban Building Function Dataset (GBA-UBF), a large-scale, fine-grained dataset that assigns one of five functional categories to nearly four million buildings across six core GBA cities. We proposed a Multi-level Building Function Optimization (ML-BFO) method by integrating Points of Interest (POI) records and building footprints through a three-stage pipeline: (1) candidate label generation using spatial overlay with proximity weighting, (2) iterative refinement based on neighborhood label autocorrelation, and (3) function-related correction informed by High-level POI buffers. To quantitatively validate results, we design the Building Function Matching Index (BFMI), which jointly measures categorical consistency and distributional similarity against POI-derived probability heatmaps. Comparative experiments demonstrate that GBA-UBF achieves significantly higher accuracy, with a BMFI of 0.58. This value markedly exceeds that of the baseline dataset and exhibits superior alignment with urban activity patterns. Field validation further confirms the dataset's semantic reliability and practical interpretability. The GBA-UBF dataset establishes a reproducible framework for building-level functional classification, bridging the gap between coarse land use maps and fine-grained urban analytics. The dataset is accessible at https://github.com/chenchs0629/GBA-UBF, and the data will undergo continuous improvement and updates based on feedback from the community.

Problem

• Classifying building functions at fine-grained scale for urban planning
• Addressing coarse granularity in existing land use datasets
• Capturing intra-block functional heterogeneity in rapidly urbanizing areas

Innovation

• Multi-level optimization integrating POI and building footprints
• Three-stage pipeline with spatial overlay and iterative refinement
• Building Function Matching Index for quantitative validation