Decoding Urban Growth and Road Networks: Geospatial Insights from Bangalore’s Evolving Metropolis

The rapid urbanization of Indian cities driven by economic liberalization and industry centric growth has resulted in significant transformation of land use and land cover within urban areas and their surrounding regions. This surge in urbanization has exerted substantial pressure on the management of urban land use and infrastructure, exacerbating environmental conditions and have given rise to complex social dynamics. Therefore, understanding the urban evolution and drivers of urban growth are vital for assessing the impacts of urban land use on sustainable urban development. We have utilized cutting-edge geospatial technologies to outline, measure, validate and forecast urban expansion, ultimately offering real-time solutions to address the challenges encountered by developing cities. Spatio-temporal patterns of urban expansion were analysed and quantified the shifts in growth patterns of urban hotspots attributed by specific events or triggers in Bangalore City. The outcomes suggested a decentralised approach for setting up new hubs for increasing economic activities in the form of satellite towns and shifting future industrial corridors to other small and medium-sized towns and cities. Moving forward in this direction, the Ministry of Urban Development, Government of India have already introduced an Integrated Development of Small and Medium Towns (IDSMT) scheme that aims to encourage the planned and sustainable growth of India’s small and medium-sized towns and cities. These smaller towns and cities may act as growth hubs for the nearby regions to promote inclusive growth and balance regional development. As an attempt to estimate the transportation infrastructure requirements (road network density) of future cities, we have used human settlement indices (landscape structures metrics) to predict transportation index. A novel two-step hybrid framework called RidgeGAN (Generative Adversarial Networks) is proposed by combining Kernel Ridge Regression (KRR) and CityGAN for predicting network density in both real and simulated cities. The experimental outcomes are aimed to assist the Government in making informed decisions in transportation development, thereby creating more liveable and sustainable areas. Through the interdisciplinary research on urban land use analysis, event triggered urban expansion, urban growth modelling, topological assessment of road networks and prediction of transportation measures, our research showcases the multifaceted nature of urbanization in the modern era. It is a step towards analysing current challenges in existing cities and predicting important metrics for sustainable future cities that would foster equity and resilient urban structures.