Land Cover Segmentation

Land cover segmentation uses computer vision to automatically classify different land cover types (e.g., forests, urban areas, water) in remotely sensed imagery, primarily aiming to create accurate and up-to-date land cover maps. Current research emphasizes improving segmentation accuracy through multi-modal data fusion (combining satellite imagery with aerial photos or other data sources), leveraging self-supervised and transfer learning techniques to address data scarcity and annotation challenges, and employing advanced architectures like U-Net, LinkNet, and Vision Transformers. These advancements are crucial for applications in environmental monitoring, urban planning, and precision agriculture, enabling more efficient and informed decision-making.