Spatial-Temporal analysis of urban environmental variables using building height features

The 11th Sustainable Development Goal (SDG) is focused on sustainable cities and communities and is closely related to other SDGs such as Good health and well-being (the third SDG) and climate action (the 13th SDG). However, the lack of data has made it difficult to evaluate the success of reaching these goals. To address this, a method is proposed in this paper to generate temporal building height maps and extract features from the 3D structure of urban areas to examine their relationship with environmental variables, acquired from remote sensing satellites. Therefore, no survey data is required from the study area. Building height map is generated by processing Sentinel-1, Sentinel-2, and Nighttime light data by a UNet-based deep model. The results showed significant improvements in Mean Square Errors compared to available building maps in Berlin and London. In the second step, several features were extracted from the 3D structure of urban areas, and their relationship with environmental variables such as atmosphere contents from Sentinel-5 data and Urban Heat Island (UHI) from MODIS was examined via shallow regression models. The spatial study shows high correlation between each environmental variable and height map features in a neighborhood, with R2 scores of 0.78, 0.94, 0.92, 0.7, and 0.88 for CO2, CO, NO2, SO2, and UHI, respectively. It is found the environmental parameters are shaped by the collective building heights within a specific neighborhood, rather than hinging on the individual building heights at the sampling site. Furthermore, spatial resolution plays a significant role. In the case of the MODIS-based heat island map, a 3 km neighborhood yields a high R2-score, whereas when utilizing Sentinel-5 data, it is advisable to employ a larger neighborhood. Furthermore, the temporal study shows even higher R2 scores than the spatial domain, indicating the temporal reliability of the proposed method. The findings of this study can be used by governors and decision makers for sustainable urban development.