<![CDATA[Urban heat island (UHI) intensity in rapidly urbanizing Indonesian cities is commonly assessed through land surface temperature (LST) derived from satellite thermal sensors, yet fine-resolution, machine-learning-based spatial prediction for small and medium cities in the Bandung Raya region remains limited. This study applies a geospatial artificial intelligence (GeoAI) workflow to model and map LST in Kota Cimahi, West Java, using four cloud-screened Landsat 8/9 Collection 2 Level-2 thermal scenes acquired between July and August 2025, resampled to the 10 m grid of Sentinel-2-derived NDVI, NDBI, and NDWI and Dynamic World built-up density and land-cover layers. Random Forest (RF) and Support Vector Regression (SVR) models were trained on 627,687 pixels and evaluated on 269,009 independent pixels. RF achieved a marginally higher test R² (0.528; RMSE 2.17 °C; MAE 1.63 °C) than SVR (R² 0.525; RMSE 2.18 °C; MAE 1.61 °C). Built-up density and NDBI dominated under RF impurity-based importance, whereas NDVI dominated under SVR permutation importance, reflecting strong multicollinearity among the optical predictors. Spatial prediction confirmed a consistent thermal gradient, with the highest LST concentrated in the built-up southern core and the lowest in the vegetated northern highlands, although one acquisition date relied on cross-date gap-filling for over 90% of its pixels because of persistent cloud cover — a limitation reported transparently in this article rather than concealed.]]>
