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Semantic segmentation is a computer vision technique that involves labeling each pixel of an image with a class, enabling fine-grained understanding of the scene and objects within it. In the context of deep learning algorithms, semantic segmentation models typically employ encoder-decoder architectures or fully convolutional networks to learn hierarchical feature representations and produce high-resolution predictions for each pixel. These techniques are particularly useful in applications such as autonomous driving, medical imaging, and satellite image analysis, where accurate object detection and scene understanding are crucial. As a result, semantic segmentation represents an essential component of advanced artificial intelligence and technology systems that rely on deep learning algorithms for sophisticated visual perception tasks.