Prediction of summer daytime land surface temperature in urban environments based on machine learning

Land Surface Temperature (LST) is an important indicator of urban heat environments and can be largely influenced by the morphology factors of cities. However, previous studies mainly focus on large-scale and coarse-grained forecast modeling, making it hard to inform architects and urban designers without the advantage of quick, fine-grained prediction and visualization. The paper uses Generative Adversarial Networks (GAN) to address this gap by proposing a prediction model of city plans and corresponding LST heat maps. Taking New York City as an example, we use the Light Detection and Ranging (LiDAR) data, Landsat Surface Temperature data, and other relevant data to build seven hundred image pairs as the training set to train the model of predicting LST distribution. Using untrained pairs as the test set, the model can generate LST maps relatively quickly and accurately with the input of city plans. Then after accuracy analysis, different scenarios are simulated to test the application of the model in predicting the environmental impacts of plan modifications on land surface temperature. Eventually, the principles proposed in this paper can be applied to the development of relevant interactive design and planning tools in the future.

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