PROMETHEUS: City-scale material mapping with large vision models for emissivity-based airborne thermography

Deriving land surface temperatures (LST) from aerial thermography requires surface emissivity information, which is typically assumed uniform despite considerable variation across urban materials. We present PROMETHEUS, a workflow that uses a fine-tuned Large Vision Model (LVM) to produce city-scale material classification at airborne resolution. This classification enables emissivity-based LST estimation following the GRAZ method, which uses three-dimensional Monte Carlo sampling to determine view factors for reflected thermal radiation and models elevation-dependent atmospheric transmittance, upwelling and downwelling radiation. We applied this workflow to a 100×100 km area centred on Klagenfurt, Austria, where thermal infrared imagery at 1 m resolution was acquired on August 10-11, 2024 during a summer heat period, with daytime and nighttime flights at 1600 m altitude. A team of 12 surveyors collected concurrent in-situ land and water surface temperatures across 13 stations throughout the city. Using existing 5 cm RGB and near-infrared orthoimagery combined with photogrammetric building segmentation, expert annotators labelled rooftop materials across 30 classes via a collaborative platform with a standardized material guide. These labels were used to fine-tune an LVM that then classified materials across the full study area. The output was merged with municipal land cover data and converted to emissivity values using a look-up table derived from spectral libraries. Atmospheric parameters were obtained from ECMWF profiles. Comparison with in-situ measurements shows improved LST retrieval relative to uniform emissivity assumptions, particularly for low-emissivity surfaces such as metal roofing. This workflow demonstrates a practical approach for scaling limited expert annotations to city-wide material mapping.