Semantic segmentation is applied in scenarios requiring pixel-level understanding of images. In autonomous vehicles, it is used to identify and differentiate between road elements, such as lanes, pedestrians, and vehicles. In medical imaging, semantic segmentation helps in identifying regions of interest, such as tumors or organs, in X-rays or MRI scans. Other applications include agriculture (e.g., plant and soil segmentation), environmental monitoring (e.g., land-use classification), and video analytics (e.g., activity recognition). The ability to assign meaningful labels to each pixel makes semantic segmentation valuable in diverse domains.
Where do you apply the concept of 'semantic segmentation'?
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