A 100m Gridded Anthropogenic Heat Inventory Dataset for the Belt and Road Region

Data Introduction Anthropogenic heat (AH) is heat generated by human activities. Anthropogenic heat emission (AHE) has an important impact on urban thermal environment, atmospheric circulation, air quality, and even regional climate. Accurate estimation of AH is of great significance to the study of human activities affecting the climate and environment. In recent years, the rapid development of information technology and the availability of massive amounts of data have made machine learning a powerful tool for estimating anthropogenic heat fluxes (AHF) and assessing their impacts. Multi-source remote sensing data are also widely used to obtain more detailed spatial and temporal distribution characteristics of AH. Despite recent advances in deep learning for geographic ecosystem monitoring, there is still a need for larger regional scale AH datasets with higher resolution for regional climate and atmospheric environment numerical simulations. Here, we combine multi-source remote sensing, meteorological, urban POI data and machine learning models to build a Belt and Road AH dataset. It contains more than 300 million small data blocks sampled from countries along the Belt and Road in 2022 with a spatial resolution of 100×100 m. Covering Southeast Asia, South Asia, West Asia, Central and Eastern Europe, Central Asia and the Commonwealth of Independent States 64 countries.