FastFlow: AI for Fast Urban Wind Velocity Prediction

Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains, including computer vision and natural language pro-cessing. These extend to various areas in sustainability, including for satellite image analysis to obtain information such as land usage and extent of development. 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 compu-tationally expensive and time-consuming high-fidelity numerical simulations each time. This fast surrogate can then be potentially integrated into other design optimization frameworks, including generative models or other gradient-based methods. Here we present an investigation into the use of convolutional neural networks as a surrogate for urban layout characterization that is typically done via high-fidelity numerical simulation. We then 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 neural network trained on this data-set, demonstrating test errors of under 0.1 m/s for previously unseen novel 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. In addition, it is hoped that this data set will further inspire, facilitate and accelerate research in data-driven urban AI, even as our baseline model facilitates quantitative comparison to future, more innovative methods.