Enhancing Urban Heat Monitoring: Assessing the Contribution of High-Density Environmental Sensors in AI-Based Models

High summer air temperatures lead to increased morbidity and mortality in urban areas worldwide.　To mitigate the adverse effects of extreme heat, policymakers and urban planners have developed high-temperature adaptation strategies and public health management plans. The effective implementation of these measures requires a precise spatial understanding of heat generation patterns within urban environments. With the advancement of IoT technology, the use of third-party sensors, which can be deployed and operated at relatively low cost, has become increasingly common for monitoring urban temperatures as a method of fixed-point measurement. Some studies have suggested that data from these sensors can complement information from existing primary sensors. However, the extent to which these sensors enhance the understanding of the urban thermal environment remains an area of ongoing research.

Therefore, This study aims to quantify the contribution of high-density sensors operated by local governments to improving the performance and reliability of AI-based models for estimating high-resolution heat-related information in urban areas. Specifically, this study (1) constructs a baseline model using a commonly adopted set of auxiliary variables including land surface temperature (LST) derived from Landsat 8 satellite imagery and (2) develops an alternative model incorporating observational data from high-density sensors operated by the Seoul Metropolitan Government in South Korea as additional auxiliary variables. By quantitatively assessing the role of high-density sensors deployed by metropolitan governments, this study seeks to provide valuable insights for enhancing the accuracy of thermal hazard information. The findings will support metropolitan and local government decision-makers and urban planners in developing more effective strategies for adapting to extreme heat in the future.