Land use land cover analysis

The rapid exploitation of natural resources has caused major changes to global land surfaces, particularly in densely populated regions. This study emphasizes the urgent need for sustainable land management practices aligned with the United Nations Sustainable Development Goals (SDGs), particularly Goal 13 (Climate Action) and Goal 15 (Life on Land). Effective understanding of Land Use and Land Cover (LULC) is critical for efficient resource allocation, urban planning, and environmental management. To analyze LULC changes, this work integrates Geographic Information Systems (GIS), Machine Learning (ML), Deep Learning (DL), and hybrid approaches. Using the EuroSAT dataset, both a ResNet-50 model and a Convolutional Neural Network (CNN) were trained, with the ResNet-50 model achieving superior accuracy, thereby demonstrating its effectiveness in LULC classification. The study provides a comprehensive overview of input data, methodologies, and performance metrics, while also incorporating deforestation prediction to address the growing global concern over forest loss. By offering deeper insights into the drivers of deforestation and the challenges ahead, the findings support the formulation of improved land management and conservation strategies. Furthermore, the survey serves as a reference for researchers, guiding the selection of appropriate models tailored to specific applications.