From Heuristics to Data: Quantifying Site Planning Layout Indicators with Deep Learning and Multi-Modal Data

2025-08-15

AI Summary

Conventional site planning relies heavily on expert intuition and single-source data, hindering systematic, quantitative evaluation of multifunctional spatial layouts. Method: This paper proposes SPLI—a data-driven Site Planning Layout Indicator system—establishing a five-dimensional quantitative framework encompassing functional classification, spatial organization, functional diversity, service accessibility, and land-use intensity. SPLI transforms empirical knowledge into computable, reusable, standardized metrics. It integrates heterogeneous multi-source data—including OpenStreetMap, POIs, building morphology, land-use maps, and satellite imagery—and innovatively employs a collaborative Graph Neural Network (GNN) and Relational Graph Neural Network (RGNN) to jointly model spatial semantics and topological relationships, effectively mitigating data sparsity. Contribution/Results: Experiments demonstrate that SPLI significantly improves functional identification accuracy, providing a unified, scalable, and interpretable metric foundation for urban spatial analysis, inference, and retrieval.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data visualization

Abstract

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.

Problem

• Quantifying urban site layout patterns using multi-modal data and deep learning
• Overcoming traditional reliance on experiential judgment in site planning
• Integrating heterogeneous data sources for systematic spatial layout analysis

Innovation

• Integrates multi-source spatial data with deep learning models
• Quantifies urban layouts through five analytical dimensions
• Uses graph neural networks to address data gaps