Spatio-temporal variation of building morphology in Indian Cities from 2018 to 2023
摘要
By 2050, about two-thirds of the world’s population will live in urban areas, intensifying resource consumption in cities. Depending on urban sprawl and building morphology, this can lead to adverse environmental and public health effects, including increased energy consumption and the Urban Heat Island effect. While urban sprawl in India has been studied in terms of land-use changes, systematic assessments of spatio-temporal variability in building morphology remain limited. The current study addresses this gap by examining the evolution of building morphology in 11 major Indian cities over two years: 2018 and 2023. Remote sensing data, coupled with a deep learning model, Simultaneous building Height And footprinT extraction from Sentinel imagery (SHAFTS), produces building height and footprint maps at 100 m resolution. Variability index (VI), defined as the rate of change in spatial autocorrelation with shift distance, was used to quantify urban heterogeneity. In 2023, the mean footprint and height VIs were 0.0481 km−1 and 0.0598 km−1, indicating that height variability was ~ 24% greater than footprint variability. Between 2018 and 2023, the footprint variability decreased by ~ 5%, whereas height variability increased by ~ 4%. Chandigarh (0.0557 km−1) and Mumbai (0.0351 km−1) have the highest and the lowest footprint variability, respectively, while Bangalore (0.0739 km−1) and Mumbai (0.0441 km−1) have the highest and lowest height variability, respectively, in 2023. The divergence between footprint and height variability suggests increasing vertical densification. These findings provide insights into evolving urban form and a scalable approach for linking building morphology with urban heat, airflow, and building energy demand.