Urban Growth Dynamics Using Cloud-Based Geospatial Analysis in Google Earth Engine: Udaipur, India Dr. Urmi Sharma
Assistant Professor, Department of Geography, Mohanlal Sukhadia University Udaipur, India
Abstract
Urbanization is a major anthropogenic driver of land use land cover change, with impervious surface mapping serving as a robust proxy for settlement expansion. In India, the urban population has risen from 17 percent in 1951 to nearly 35 percent in 2021, and Udaipur exemplifies this transformation. Classified as a Tier-2 city, its Urban Agglomeration spans 109.4 sq km and has grown from 474,531 inhabitants in 2011 to an estimated 692,000 in 2025 (46 percent growth). Its strategic location, economic profile, and rapid population growth driven by tourism and migration make it a critical case for studying spatial expansion in medium sized Indian cities.
This study examines built-up expansion in the Udaipur Development Area (UDA) from 1988 to 2018 using the Tsinghua FROM-GLC Year of Change to Impervious Surface dataset (30 m resolution, greater than 90 percent accuracy) processed in Google Earth Engine (GEE). Ancillary datasets-OpenStreetMap roads and lakes, SRTM DEM, and UDA boundaries-were integrated. Built-up maps for 1988, 1998, 2008, and 2018 were generated, with density classes (high, moderate, low, dispersed) derived using r.neighbour in QGIS. Elevation zonation and proximity to transport networks were assessed through GIS overlay and zonal statistics.
Results reveal a twelvefold increase in built-up area from 4.91 sq km (1988) to 61.67 sq km (2018), with 78 percent of growth between 500-600 m MSL. Peripheral zones shifted from low to moderate densities, reflecting peri-urban transformation and integration into the metropolitan core. Expansion is constrained by the surrounding Aravalli hills, limiting higher elevation development. The availability of ready-to-use, high-accuracy, multi-temporal datasets in GEE enabled efficient, reproducible analysis using open-source tools. Extending such high-resolution datasets beyond 2018 is essential for predictive urban growth modelling to support sustainable planning in rapidly growing cities.
Keywords: Urban expansion, Google Earth Engine, QGIS, Udaipur
