Predicting Urban Land Use and Mitigating Land Surface Temperature: Exploring the Role of Urban Configuration with Convolutional Neural Networks

The objective of this research was to examine the influence of urban configuration on the mitigation of land surface temperature (LST) and the prediction of land use and land cover change through the utilization of convolutional neural network modeling. The results indicate that the formation of different urban heat island patterns is significantly influenced by both urban geometry and land use land cover (LULC) types. However, there is no significant correlation between these factors and LST across all configuration metrics. The associations between landscape configuration and land cover types exhibit variability contingent upon the particular forest cover categories under examination. Furthermore, the application of predictive LULC mapping reveals a divergent pattern, characterized by a rise in the overall extent of vegetation but a decline in the inner context of the Shiraz metropolitan area. The projected trajectory of built-up areas indicates a continued trend of urban expansion. The unique landscape patterns are a result of the distinct characteristics of each LULC. According to recommendations, to address the issue of mean LST, it is advisable for urban landscape planning to give priority to cohesion, density, and continuity while simultaneously minimizing fragmentation, variability, and complexity.