Urban Heat

Urban heat, characterized by significantly higher temperatures in urban areas compared to surrounding regions, is a critical research area focusing on understanding its causes, impacts, and mitigation strategies. Current research employs advanced machine learning techniques, including convolutional neural networks and LightGBM models, coupled with remote sensing data (e.g., LiDAR, Sentinel-3 satellite imagery, and thermal imaging) to map and predict urban heat patterns at high spatial resolutions. This work is crucial for improving urban planning and design, enhancing public health outcomes by mitigating heat-related illnesses, and informing sustainable development initiatives aimed at reducing the urban heat island effect. Studies are also exploring the effectiveness of green infrastructure, such as roof greening, in reducing urban heat, though its impact appears to be more limited than initially anticipated.