Drone Photogrammetry-based Wind Field Simulation for Climate Adaptation in Urban Environments

Abstract

Addressing climate change issues is one of the most important tasks within the United Nations Sustainable Development Goals. Accurate and efficient simulation of wind fields within cities is essential for climate adaptation. Traditional simplified geometric model-based wind flow simulation can lead to significant errors, affecting the ability to develop effective urban climate strategies. This study addresses this limitation by introducing a novel workflow that leverages drone photogrammetry, deep learning, and geometric complexity quantification to create highly detailed 3D models of in-use building clusters within cities. These models are subsequently used for computational fluid dynamics simulations to accurately predict urban wind fields. The proposed method was validated on three real-world building clusters. Compared to traditional footprint extrusion models, the proposed method demonstrates an average error reduction of 29.2% in large eddy simulation cases and 17.6% in steady Reynolds-averaged Navier-Stokes equations cases. Meanwhile, the proposed model improved computational efficiency by an average of 33.7% in large eddy simulations compared to the flashy oblique photography model. The proposed method provides a balanced model of accuracy and efficiency for urban flow simulations. It has the potential to be incorporated into computational fluid dynamics best practice guidelines, thereby promoting the development of climate-resilient cities.