Land Use Land Cover

Land Use/Land Cover (LULC) mapping uses satellite and aerial imagery to classify Earth's surface into different categories like urban areas, forests, and agricultural lands. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and variations like U-Net and Inception-based architectures, often incorporating techniques like semi-supervised learning to address limitations of labeled data. These advancements improve the accuracy and efficiency of LULC mapping, providing crucial data for urban planning, resource management, and monitoring environmental change, ultimately supporting sustainable development initiatives.