Mapping High-Resolution Building Development Over Delhi Ncr Using Sentinel-2

Deepika Mann, Jonathan Prexl, Sudipan Saha, Michael Schmitt

Abstract: In recent decades, rapid urbanization in India, fueled by population growth, has spurred the construction of new cities and the expansion of existing urban centers, extending into larger peripheral areas—a trend common in developing nations globally. Despite its widespread occurrence, accurately mapping human settlements and building distributions remains a challenge. State authorities and private enterprises, including Microsoft and Google, have sought to comprehensively capture this data. While existing initiatives offer a global perspective, challenges persist, especially in precision, for countries like India where cities boast highly dense and mixed urban development. This study explores the potential of Sentinel-2 images for building footprint mapping and change detection at 2.5 m spatial resolution, focusing on the dynamic Delhi National Capital Region (NCR). Recent works demonstrate sub-pixel accuracy in deriving building footprint maps through deep learning on Sentinel-2 imagery. The research aims to develop on existing findings taking into account seasonal variations and using improved training labels to further extend these findings to India.