Supervised Segmentation

Supervised segmentation is a machine learning technique aiming to automatically partition images or 3D data into meaningful regions based on labeled training data. Current research emphasizes improving accuracy and efficiency, exploring various model architectures including U-Net variations, transformers, and even simpler shallow models like LightGBM, depending on the data and application. This field is crucial for diverse applications, ranging from medical image analysis (e.g., organ segmentation) and remote sensing (e.g., identifying pollution plumes) to infrastructure monitoring (e.g., building extraction), driving advancements in numerous scientific and technological domains. Furthermore, research is actively exploring ways to reduce reliance on extensive pixel-level annotations, investigating weakly supervised and self-supervised approaches to make the process more scalable and efficient.