Air temperature estimation through thermal satellite imagery using uncertainty-integrated transformers
L.W. van Blokland (TU Delft - Architecture and the Built Environment)
A. Rafiee – Mentor (TU Delft - Architecture and the Built Environment)
R.C. Lindenbergh – Mentor (TU Delft - Civil Engineering & Geosciences)
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Abstract
Accurate estimates for air temperatures in urban environments can help with timely and precise action against the urban heat island (UHI) effect. Uncertainty-aware spatio-temporal transformers are an ideal candidate model for producing such predictions. This study details the implementation and testing of a transformer model that combines remotely sensed land surface temperature (LST) data with in-situ sensor readings to predict air temperature values for the entirety of the Netherlands. The three best models out of the 21 trained attained an averaged test-set error of 1.365° Celsius MAE. Model inference has produced Geotiffs of air temperature and uncertainty predictions for all of the Netherlands, at a 70m by 70m pixel resolution. NASA's ECOSTRESS dataset supplied LST imagery and assorted ancillary bands, ESA's Copernicus provided NDVI and Landcover data, and sensor readings were acquired from the royal Dutch weather service (KNMI). Overall this study details the design for a spatio-temporal transformer model that produces uncertainty-aware air temperature estimations.