Automated Generation of Tree-Aware Urban Shade Maps using Deep Learning

Abstract

Ever increasing global heat has necessitated more robust urban shade analysis in order to keep, among other applications, pedestrians safe and comfortable. Specifically, the impacts of shade-trees on the urban shade landscape is a topic of growing interest and research, and new tools and workflows are being developed in order to allow urban planners to have the clearest understanding of how to best provide shade to citizens. Geometric shade calculation techniques have been used for some time, but they are slow to run at scale and can involve convoluted workflows that slow down analysis. Thus, this thesis investigates the applicability of deep learning to generate shade rasters. The goal is to see if deep learning can allow for faster shade map derivations and a smoother workflow, without sacrificing accuracy. Furthermore, it is the first research of its kind to consider a purely deep learning based approach in deriving tree-aware urban shade maps which capture the contributions of shade trees.

This thesis presents a Conditional Generative Adversarial Network based off of the pix2pix framework, and explores four different combinations of input data and how they affect the final model performance. The model can produce shade rasters ~25 - 45.5% faster than the geometric approach, depending on the parameters used when generating outputs. It also does this using less data and less preprocessing than the geometric alternative. However, while it produces very convincing rasters in some cases, the outputs have insufficient temporal coherence to serve as a reliable alternative to the geometric approach. Thus, while it serves as an interesting foundation for future research, the model as presented in this report is not sufficient to replace geometric shade derivation techniques.