Segmentation Map

Segmentation maps are digital representations that partition an image or volume into distinct regions based on shared characteristics, primarily aiming to delineate objects or structures of interest. Current research focuses on improving segmentation map accuracy and robustness using various techniques, including deep learning architectures like U-Net and transformers, self-supervised and weakly supervised learning methods to reduce reliance on extensive labeled datasets, and incorporating contextual information from other modalities (e.g., language models, depth maps) to enhance segmentation quality. Accurate and efficient segmentation maps are crucial for numerous applications, ranging from medical image analysis (e.g., tumor detection, organ segmentation) to autonomous driving (e.g., lane detection, object recognition) and remote sensing (e.g., building footprint extraction, urban forest monitoring).