Continuous Sky View Factor Calculations Using a Parallel GPU Workflow

The Sky View Factor (SVF) is a key parameter in urban climate modelling, which quantifies the fraction of the visible sky from a given point and allows for the estimation of incident solar radiation, thermal comfort, and urban heat distribution. High resolution in SVF computation is essential for microclimatic studies in the canopy layer, where detailed representations of urban environments are crucial for understanding variations in heat exposure. Traditional SVF calculation methods often rely on sequential processing of shadow projections, however, these methods are computationally intensive and time-consuming, particularly for urban climate analyses at high resolution.

These computational challenges are further magnified when incorporating complex components such as vegetation, where tree crowns exhibit intricate geometries, partial transparency, and permit sky visibility from beneath the canopy. These properties require detailed modeling to account for light penetration and obstruction. This significantly increases the computational cost of Sky View Factor calculations and extends runtime to hours or even days, depending on scale and resolution.

This study introduces a novel GPU-accelerated ray tracing approach for SVF calculation, designed to address the computational limitations of traditional methods for large-scale analyses. By utilizing NVIDIA GPUs and the CUDA programming framework, the method applies parallel computing to perform ray tracing across the full range of azimuth and altitude angles. It estimates SVFs by systematically weighting blocked rays based on their spatial contributions to the hemisphere.

The accuracy of the developed method is validated through two complementary approaches. First, modelled SVF values are compared against theoretical expectations derived from idealized geometric environments. Additionally, a test case on a neighbourhood in Rotterdam is conducted to compare the results of the developed method against those obtained using an established SVF estimation technique using a serial approach. In addition to accuracy, computational efficiency is evaluated by comparing processing times across different study area extents with those of a CPU-based implementation. The proposed GPU workflow achieves a 99% reduction in processing time compared to traditional shadow casting methods performed on a CPU, while maintaining similarly high resolution and accuracy.