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<p>Digital 3D city models support urban planning, simulation, and immersive applications, but common production methods such as manual CAD modelling, photogrammetry/LiDAR, and GIS-based extrusion are often slow, costly, difficult to scale, and hard to update. Procedural modelling offers a scalable alternative, yet practitioners still need clear guidance on when to use it and how to evaluate its outputs. This paper presents and evaluates a CityEngine workflow that combines open geospatial inputs with rule-based generation of road networks, blocks and parcels, and buildings. Using a commodity laptop (Intel Core i5‑3210&#xa0;M, 6&#xa0;GB RAM) and CityEngine, we generated three canonical morphologies (organic, raster/grid, and radial) over an area of approximately 2 km<sup>2</sup> under both default and dense scenarios. We report computational performance metrics, including generation time, peak RAM, CPU seconds, exported file size, and polygon count, and we complement these with output checks aimed at plausibility and visual realism for each morphology. Compared with a traditional modelling workflow, procedural generation reduces production time by one to two orders of magnitude while keeping resource use within desktop limits. Based on the case study results, we derive a decision matrix (Table&#xa0;<InternalRef RefID="Tab3">3</InternalRef>) that compares procedural modelling with photogrammetry/LiDAR, GIS extrusion, and manual/CAD approaches across criteria such as time, scalability, update cadence, and required visual detail. This synthesis positions procedural modelling as a practical middle ground and motivates hybrid workflows that combine procedural background fabric with data-driven and manual elements when projects must balance fidelity, cost, and the frequency of updates.</p>

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Procedural generation as an approach for digital representation of a city

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

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  • Farshad Shariatpour,
  • Amir Shakibamanesh,
  • Morteza Rahbar

摘要

Digital 3D city models support urban planning, simulation, and immersive applications, but common production methods such as manual CAD modelling, photogrammetry/LiDAR, and GIS-based extrusion are often slow, costly, difficult to scale, and hard to update. Procedural modelling offers a scalable alternative, yet practitioners still need clear guidance on when to use it and how to evaluate its outputs. This paper presents and evaluates a CityEngine workflow that combines open geospatial inputs with rule-based generation of road networks, blocks and parcels, and buildings. Using a commodity laptop (Intel Core i5‑3210 M, 6 GB RAM) and CityEngine, we generated three canonical morphologies (organic, raster/grid, and radial) over an area of approximately 2 km2 under both default and dense scenarios. We report computational performance metrics, including generation time, peak RAM, CPU seconds, exported file size, and polygon count, and we complement these with output checks aimed at plausibility and visual realism for each morphology. Compared with a traditional modelling workflow, procedural generation reduces production time by one to two orders of magnitude while keeping resource use within desktop limits. Based on the case study results, we derive a decision matrix (Table 3) that compares procedural modelling with photogrammetry/LiDAR, GIS extrusion, and manual/CAD approaches across criteria such as time, scalability, update cadence, and required visual detail. This synthesis positions procedural modelling as a practical middle ground and motivates hybrid workflows that combine procedural background fabric with data-driven and manual elements when projects must balance fidelity, cost, and the frequency of updates.