Leveraging Artificial Intelligence (AI) and Geospatial Technologies for Community-Centered Urban Expansion Forecasting in Hyderabad

Abstract

Urban expansion poses significant challenges for rapidly growing cities like Hyderabad, particularly in vulnerable communities where unplanned development strains infrastructure, environment, and public services. This study integrates Artificial Intelligence (AI) and Geospatial Technologies—Remote Sensing (RS) and Geographic Information Systems (GIS)—to build a predictive model for urban growth with a focus on supporting community service planning. Using satellite imagery, demographic data, and socio-economic indicators, machine learning algorithms such as Random Forest, SVM, CNN, and LSTM were applied to analyze spatial-temporal patterns. The model forecasts urban expansion because data-driven insights are crucial for anticipating needs in housing, sanitation, and public health. Validation against historical trends demonstrates strong predictive performance. The outcomes aim to empower local governments and community organizations to proactively allocate resources, protect ecological zones, and improve residents’ quality of life. This approach bridges technology and public service to promote sustainable, inclusive, and resilient urban development in Hyderabad.