Wind comfort is an important factor when new buildings in existing urban areas are planned. It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an 𝐹1-score greater than 80% and can be combined with any wind rose statistic.