Multi-Model AI-Driven Satellite Image Segmentation for Comprehensive Urbanization and Environmental Degradation Analysis in Bangladesh

The accelerated urbanization of Bangladesh has hence influenced the structure and systematic composition of landscapes and ecosystems, posing an intricate challenge for managers and urban planners. This study highlights a need for sophisticated monitoring with the application of AI and deep learning for intensive image processing and annotation. We built a massive dataset of satellite imagery from various parts of Bangladesh by collecting, processing, and annotating it to outline the buildings, trees, water bodies, tin shades, and farmlands. This dataset underpins the analyses of landscape temporal transformations, urban sprawl, environmental attrition, and land-grab within defined multiples of years over the preceding decade. Our AI framework incorporates four cutting-edge deep learning models and ensures unparalleled accuracy via robust cross-validation with optimized object detection capabilities. Despite the challenges posed by data annotation, our method still holds immense promise for improving artificial intelligence in satellite image analysis. For future work, we aim to develop a larger dataset, overcome the issues in annotation, and include several models to improve the ranking of our results-an area where this study brings invaluable contributions to urban environments, environmental conservation, and sustainable development.