Taking Xiamen, a densely populated city with rapid economic development, as an example, this study employs surface temperature data derived from MODIS remote sensing data optimized through remote sensing and artificial intelligence technologies to extract urban heat island and vegetation coverage information. The study conducts dynamic monitoring and spatiotemporal analysis of the urban heat island in the study area. Taking Yunnan Province as another example, based on Landsat OLI/TM and NPP_VIIRS remote sensing data, the study extracts urban built-up area information and examines the expansion characteristics of urban built-up areas from three aspects: the number of expansions, spatial distribution patterns, and nighttime light scale, covering the period from 2000 to 2020. Remote sensing data based on urban heat islands indicate that high-temperature zones are primarily distributed in densely populated urban areas with developed industries and commerce, while secondary high-temperature zones are scattered in the urban-rural fringe areas surrounding high-temperature zones. Areas with better water bodies and vegetation exhibit low-temperature and secondary low-temperature conditions. For example, parts of Xiamen’s urban core are primarily controlled by high-temperature and secondary high-temperature zones. Remote sensing data based on urban expansion indicate that there are significant differences in expansion rates and intensities across different cities and stages. For instance, the expansion intensity of the Dianzhong Urban Agglomeration generally follows a trend of “first decreasing—then increasing—and finally decreasing again.”