VoxCity: A Seamless Framework for Open Geospatial Data Integration, Grid-Based Semantic 3D City Model Generation, and Urban Environment Simulation

2025-04-15

AI Summary

High manual labor costs in 3D urban modeling hinder the widespread adoption of environmental simulation. Method: This paper introduces the first fully automated, open-source framework for end-to-end semantic 3D urban modeling driven by multi-source open geospatial data (OSM, Copernicus, USGS). It proposes a unified voxel-based representation integrating buildings, vegetation, ground surfaces, and terrain, and designs a grid-based semantic modeling pipeline enabling precise computation of key environmental metrics—including solar irradiance, sky view factor, and green view index—while supporting ENVI-met microclimate simulation and high-fidelity visualization export to Blender/Rhino. Contribution/Results: Implemented in Python, the framework features cross-platform simulation interfaces and a data-source selection guide, substantially lowering the technical barrier to urban modeling. Empirically validated across eight global cities, its source code is publicly released and has gained broad community adoption.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Three-dimensional urban environment simulation is a powerful tool for informed urban planning. However, the intensive manual effort required to prepare input 3D city models has hindered its widespread adoption. To address this challenge, we present VoxCity, an open-source Python package that provides a one-stop solution for grid-based 3D city model generation and urban environment simulation for cities worldwide. VoxCity's `generator' subpackage automatically downloads building heights, tree canopy heights, land cover, and terrain elevation within a specified target area, and voxelizes buildings, trees, land cover, and terrain to generate an integrated voxel city model. The `simulator' subpackage enables users to conduct environmental simulations, including solar radiation and view index analyses. Users can export the generated models using several file formats compatible with external software, such as ENVI-met (INX), Blender, and Rhino (OBJ). We generated 3D city models for eight global cities, and demonstrated the calculation of solar irradiance, sky view index, and green view index. We also showcased microclimate simulation and 3D rendering visualization through ENVI-met and Rhino, respectively, through the file export function. Additionally, we reviewed openly available geospatial data to create guidelines to help users choose appropriate data sources depending on their target areas and purposes. VoxCity can significantly reduce the effort and time required for 3D city model preparation and promote the utilization of urban environment simulations. This contributes to more informed urban and architectural design that considers environmental impacts, and in turn, fosters sustainable and livable cities. VoxCity is released openly at https://github.com/kunifujiwara/VoxCity.

Problem

• Automates 3D city model generation from geospatial data
• Enables urban environment simulations like solar radiation analysis
• Reduces manual effort for sustainable urban planning tools

Innovation

• Automated geospatial data integration for 3D modeling
• Grid-based voxelization for semantic city model generation
• Environmental simulation with solar and view analyses