Land Cover

Land cover mapping, the process of classifying Earth's surface into different categories (e.g., urban areas, forests, water bodies), is crucial for environmental monitoring, urban planning, and resource management. Current research emphasizes improving the accuracy and efficiency of land cover classification using advanced deep learning models, such as U-Net, Transformers, and Graph Neural Networks, often incorporating multispectral and hyperspectral imagery, and addressing challenges like limited labeled data through techniques like few-shot learning, self-supervised learning, and domain adaptation. These advancements are significantly impacting various fields by providing more accurate and timely information for decision-making related to climate change, biodiversity, and sustainable development.