A generative AI model of urban spaces in the face of climate change

Abstract

Urban land use structures have impact on local climate conditions. To shed light on the mechanisms of local climate w.r.t. urban land use, we present a data-driven, deep learning approach based on airborne LiDAR data statistics and the Landsat 8 satellite’s surface temperature product. Our study proposes a deep neural network architecture and data workflow to model the following question: How to vary a geospatial scene’s ambient temperature by modifying without bias its urban land use structures represented by LiDAR statistics? We model the phrase modify without bias by a constraint that allows all LiDAR statistics features to contribute to modelled temperature variations at same order of magnitude. In contrast to regular deep learning neural network optimization, we consider fixed model parameters, but variation of model input data, i.e. LiDAR statistics. The novelty of this thesis comprises of the introduction and evaluation of a deep neural network architecture that correlates vegetation and ambient temperatures from remote sensing modalities. The concept helps to approximate the climate resilience of urban areas. By analyzing numerous vegetation vs. temperature change pairs, we develop a statistical evaluation procedure to perform correlation analysis. The approach generates a qualitative answer to the question posed above: When statistically averaged over New York City with focus on the Queens borough, an increase in vegetation correlates with a decrease in ambient surface temperature with likelihood of 95%. Our contribution likes to inspire the development of urban heat island mitigation strategies in the face of climate change.