Urban areas worldwide are experiencing increase in temperatures due to urbanisation and, leading to the effect of Urban Heat Islands (UHIs), which threaten urban sustainability. Global research aims to identify UHIs and develop mitigation measures. Most existing studies rely on coarse-resolution satellite imagery, limiting the detection and characterization of heterogeneous urban surfaces and localized UHI effects. Advances in drone technology with multi-payload thermal sensors now allows LST mapping at finer spatial resolutions (<1 m), enabling detailed analysis of temperature variations across urban surfaces. Assessing the accuracy of these measurements is essential and typically involves comparing UAV-derived LST with ground-based or in situ temperature observations collected simultaneously during UAV flights. Proper calibration of the TIR sensors is necessary to minimize systematic errors. Accuracy is commonly quantified using statistical quantification like Mean Absolute Error (MAE), R squared and Root Mean Square Error (RMSE). UAVs offer much finer spatial resolution (<1 m) than satellites, enabling detection of localized UHI hotspots that coarse-resolution imagery may miss. Combining UAV, ground, and satellite data enhances confidence in LST estimates and supports precise analysis of urban heat patterns, providing critical insights for mitigation strategies and urban planning. These high-resolution datasets can support machine-learning tools for urban planners to predict localized UHI impacts, adopt mitigation strategies, and advance Sustainable Development Goals.