From multi-sensor aerial data to thermal and infrared simulation of semantic 3D models: Towards identification of urban heat islands

Urban heat islands degrade the quality of life in many urban centers. To achieve their detection in urban canopy and to predict their development in the future, infrared simulation turns out to be a suitable tool. For simulation of the temperature, various scene properties must be taken into account. Starting at raw sensor data acquired from the air, we developed an end-to-end pipeline to the semantic mesh, in which temperatures and radiance can be simulated depending on actual weather data and initial conditions and which has a potential to track the urban heat islands. To acquire the mesh, we focus on retrieving land cover classes and 3D geometry. The land cover map helps to identify buildings, to update the existing geographic maps, and to analyze building roofs with respect to their materials and thus, sustainability. The 3D geometry basically presupposes storing the scene efficiently into triangles. For each triangle, we are not only interested in material properties, but also in neighborhood relations allowing to model heat conduction. Together with terms for convection and radiation, we formulate the heat balance equation and compute the surface temperature as a function of time. The pipeline was tested on a dataset from a large Australian city exhibiting most properties which bear risks to contribute to heat islands: Its location in a subtropical (Mediterranean) climate zone, rapidly growing population, and, at least initially, a certain lack of sensibility towards sustainable management of resources and materials. To analyze both latter factors, two intermediate results from our method, namely tracking urbanization degree and identification of common roofing materials, are addressed and thoroughly evaluated in the dataset. It could be deduced that the area occupied by buildings increased by roughly 5% and that roughly every 6th building has a steel roof. Finally, high similarities with the ground truth were achieved both for temperature curves in some 20 test points and for large-scale evaluation. Deviations from the ground truth emerge in case of building roofs leading to the conclusion that the inner model assumption could be less accurate and, therefore, runs the danger to increase the urban heat island effect.