用深度学习整合多源数据，量化城市规划布局指标。
From Heuristics to Data: Quantifying Site Planning Layout Indicators with Deep Learning and Multi-Modal Data
- 融合地图、建筑、地表等多模态数据，构建空间指标体系。
- 提出五维量化框架，准确率显著优于传统方法。
- 适合城市规划与智能设计研究者参考使用。
城市地块的空间布局影响土地利用效率与空间组织。传统规划依赖经验判断和单一数据，难以系统量化多功能布局。本文提出站点规划布局指标（SPLI）系统，一种融合实证知识与异构多源数据的数据驱动框架，生成结构化城市空间信息。SPLI通过集成开放街图（OSM）、兴趣点（POI）、建筑形态、土地利用及遥感影像，支持多模态空间数据分析、推理与检索。其扩展了传统指标，涵盖五个维度：（1）建筑功能分层分类，将经验体系细化为清晰层级；（2）空间组织，量化七种布局模式（如对称、同心、轴线导向）；（3）功能多样性，以功能比率（FR）和辛普森指数（SI）实现定性评估的可度量转化；（4）基本服务可达性，结合设施分布与交通网络构建综合可达性指标；（5）用地强度，采用容积率（FAR）与建筑覆盖率（BCR）评估利用效率。数据缺失问题通过关系图神经网络（RGNN）和图神经网络（GNN）解决。实验表明，SPLI显著提升功能分类准确率，并为自动化、数据驱动的城市空间分析提供标准化基础。
原文摘要 · Abstract (English)
The spatial layout of urban sites shapes land-use efficiency and spatial organization. Traditional site planning often relies on experiential judgment and single-source data, limiting systematic quantification of multifunctional layouts. We propose a Site Planning Layout Indicator (SPLI) system, a data-driven framework integrating empirical knowledge with heterogeneous multi-source data to produce structured urban spatial information. The SPLI supports multimodal spatial data systems for analytics, inference, and retrieval by combining OpenStreetMap (OSM), Points of Interest (POI), building morphology, land use, and satellite imagery. It extends conventional metrics through five dimensions: (1) Hierarchical Building Function Classification, refining empirical systems into clear hierarchies; (2) Spatial Organization, quantifying seven layout patterns (e.g., symmetrical, concentric, axial-oriented); (3) Functional Diversity, transforming qualitative assessments into measurable indicators using Functional Ratio (FR) and Simpson Index (SI); (4) Accessibility to Essential Services, integrating facility distribution and transport networks for comprehensive accessibility metrics; and (5) Land Use Intensity, using Floor Area Ratio (FAR) and Building Coverage Ratio (BCR) to assess utilization efficiency. Data gaps are addressed through deep learning, including Relational Graph Neural Networks (RGNN) and Graph Neural Networks (GNN). Experiments show the SPLI improves functional classification accuracy and provides a standardized basis for automated, data-driven urban spatial analytics.
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