Dynamics of Land Use and Land Cover Changes in Cox’s Bazar Municipality Using GIS Based Artificial Neural Network Model

Abstract

Urban expansion in coastal municipalities has intensified land use and land cover (LULC) transformations, identifying challenges for ecological sustainability. This study explores the spatial and temporal dynamics of LULC change in Cox’s Bazar Municipality, Bangladesh, from 2005 to 2025 and predicts future scenarios for 2035 and 2045 using a GIS-based Artificial Neural Network (ANN) model. Multi-temporal Landsat imagery, Shuttle Radar Topography Mission (SRTM) elevation data, and road network layers were processed to classify five major land categories water, built-up, vegetation, agriculture, and bare land using supervised classification. Accuracy assessment specified trustworthy classification performance, with overall accuracies ranging from 78% to 84% and kappa values above 0.8. Results revealed a significant escalation in built-up areas, from 29.7% in 2005 to 39.8% in 2025, largely at the expense of vegetation, agriculture, and water bodies. Change detection underlined urban expansion agglomerated along the coastal belt and road networks, while agricultural and vegetated lands showed fragmentation and decline. ANN-based simulations projected that by 2045, built-up land will account for approximately 47% of the municipality, with vegetation and agricultural land ongoing to shrink, and water bodies fronting gradual reduction. These findings demonstrate that Cox’s Bazar is on an unmaintainable urban trajectory, driven by tourism, migration, and infrastructural development, which threatens green infrastructure and ecological resilience. The study highlights the potential of GIS–ANN modeling for urban growth prediction and highlights the urgent need for incorporated land management approaches, including zoning, ecosystem conservation, and sustainable tourism planning. By investigating both reflective and predictive understandings, this research offers critical indication for policymakers and planners seeking to balance development with environmental sustainability.