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Semantic segmentation is a deep learning technique that categorizes image pixels into pre-defined classes. The goal is to assign a label to every pixel in an image, allowing for the differentiation of objects and their boundaries in the image. This is useful in various applications, such as autonomous vehicles, medical imaging, and satellite imagery analysis. In autonomous vehicles, semantic segmentation can be used to identify road boundaries, traffic signs, and pedestrians. In medical imaging, it can be used to segment tumors and other important structures in medical scans. The approach uses convolutional neural networks (CNNs) to analyze an image and produce a segmentation map that defines the objects and their boundaries. The output is a segmented image where each pixel is labeled, clearly understanding the objects and their relationships within the image.

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