A Data-Driven Approach for Urban Heat Island Predictions: Rethinking the Evaluation Metrics and Data Preprocessing

A 2D raster data representing building volumes of each grids are derived from 3D vector-format urban data for use in machine learning applications. Since the task is to explore patterns, i.e., urban heat islands, Gaussian blurring is implemented on these generated 2D raster data before the training process. This strengthens the visual capturing of spatial relationships, and as a result the correlation rate between air temperature and building volume data is also increased. After the model training, the prediction results are not simply evaluated with most widely used shallow metrics like the Mean Square Error (MSE), but thanks to the raster format of input and output results, some image similarity metrics such as Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) that are able to detect and consider spatial relations are used during the evaluation and interpretation process, because of their higher usefulness in mimicking human visual judgements. The trained models with Random Forest and XGBoost methods which are capable of predicting the spatial distribution of air temperature by using building volume information are compared. By doing so, this research aims to assist urban planners in incorporating environmental parameters into their planning strategies, thereby facilitating more sustainable and inhabitable urban environments.