Urban Temperature

Urban temperature research focuses on understanding and predicting the spatial and temporal variations in city temperatures, particularly the urban heat island effect, to mitigate its negative impacts on human health and the environment. Current research employs advanced machine learning models, including convolutional neural networks, recurrent neural networks, and Fourier neural operators, to improve the accuracy and efficiency of temperature forecasting and microclimate simulation at various scales, from individual buildings to entire cities. High-resolution datasets of building heights and urban canopy parameters are crucial for improving model accuracy and informing urban planning strategies aimed at reducing urban heat. These advancements enable more precise simulations of urban microclimates, supporting evidence-based decision-making for urban design and climate change adaptation.