  Data-driven approaches, including deep learning, have shown great promise as
surrogate models across many domains. These extend to various areas in
sustainability. An interesting direction for which data-driven methods have not
been applied much yet is in the quick quantitative evaluation of urban layouts
for planning and design. In particular, urban designs typically involve complex
trade-offs between multiple objectives, including limits on urban build-up
and/or consideration of urban heat island effect. Hence, it can be beneficial
to urban planners to have a fast surrogate model to predict urban
characteristics of a hypothetical layout, e.g. pedestrian-level wind velocity,
without having to run computationally expensive and time-consuming
high-fidelity numerical simulations. This fast surrogate can then be
potentially integrated into other design optimization frameworks, including
generative models or other gradient-based methods. Here we present the use of
CNNs for urban layout characterization that is typically done via high-fidelity
numerical simulation. We further apply this model towards a first demonstration
of its utility for data-driven pedestrian-level wind velocity prediction. The
data set in this work comprises results from high-fidelity numerical
simulations of wind velocities for a diverse set of realistic urban layouts,
based on randomized samples from a real-world, highly built-up urban city. We
then provide prediction results obtained from the trained CNN, demonstrating
test errors of under 0.1 m/s for previously unseen urban layouts. We further
illustrate how this can be useful for purposes such as rapid evaluation of
pedestrian wind velocity for a potential new layout. It is hoped that this data
set will further accelerate research in data-driven urban AI, even as our
baseline model facilitates quantitative comparison to future methods.
