Geospatial modeling and forecasting of urban land use change using Google Earth Engine and machine learning

Urban expansion and Land Use Land Cover (LULC) change pose critical challenges for sustainable urban planning and risks to food security. This study analyzes multitemporal Landsat imagery from 1990 to 2020 for five major cities, Islamabad, Karachi, Lahore, Peshawar, and Quetta in Pakistan using the Smile Random Forest (SRF) algorithm within the Google Earth Engine (GEE) platform. Classification accuracies ranged from 86–90%, with Cohen’s Kappa coefficients between 0.86 and 0.92, demonstrating substantial to almost perfect agreement. The results reveal significant increases in urban areas: Karachi expanded from 12.4% in 1990 to 41.3% in 2020, Lahore from 15.2% to 39.8%, and Islamabad from 9.1% to 28.6%, primarily at the expense of vegetation and barren land. Elevation also influenced LULC dynamics, with higher-altitude cities like Quetta exhibiting slower but more resource-constrained urban development. A change matrix quantified class transitions, showing that urban land predominantly expanded into agricultural and vegetative land areas, raising concerns about long-term food security. Future projections using the MOLUSCE–ANN model indicate continued urban expansion by 2030, particularly in Karachi and Lahore, where built-up areas are projected to exceed 45% of total land cover. Compared with previous studies that employed Classification and Regression Trees (CART), Support Vector Machine (SVM), and CA–Markov models in single-city or short-term contexts, this study provides a multi-decadal, multi-city analysis with predictive capacity and robust validation, offering novel insights into Pakistan’s urbanization trajectory. By linking LULC change to the implications for natural resources and food security, the study contributes actionable evidence to support actions against disaster risk reduction, sustainable development and SDGs-aligned with urban policies.