A novel reference station-based methodology for high-resolution urban temperature mapping utilizing machine learning techniques

The formation of Urban Heat Islands (UHI) creates substantial effects on building energy usage alongside human comfort standards throughout the world's urban areas. Current methods for mapping urban temperatures struggle to create a balance between detailed spatial coverage and accurate time-specific data. In this research, we designed a reference station-based method to create high-resolution temperature maps of urban areas at low cost, which is implemented in Shenzhen, China as the case study. A combination of Local Climate Zone classification with satellite data and machine learning algorithms generates spatiotemporally continuous temperature field results. The XGBoost-based mapping framework can achieve an MAE of 0.56°C with an R² value of 0.980. Building simulation together with thermal comfort analysis can benefit substantially from this methodology as it allows users to develop representative high-resolution microclimates through Typical Meteorological Year (TMY) weather data. The created model enables architects and engineers and urban planners to support their decisions in building design, climate change adaptation, and energy management practices. The developed approach delivers advanced air temperature mapping at affordable costs and requires easy implementation. The proposed data collection method offers detailed temperature information with high spatial resolution and temporal precision which makes it possible to improve urban planning and forecast building as well as renewable energy system performance in urban areas.