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We propose SuperSegmentation, a unified, fully-convolutional architecture for semantic keypoint correspondence in dynamic urban scenes. The model extends SuperPoint’s self-supervised interest point detector–descriptor backbone with a DeepLab-style Atrous Spatial Pyramid Pooling head for semantic segmentation and a lightweight sub-pixel regression branch. Using Cityscapes camera intrinsics and extrinsics to construct geometry-aware homographies, SuperSegmentation jointly predicts keypoints, descriptors, semantic labels(e.g., static vs. dynamic classes), and sub-pixel offsets from a shared encoder. Our experiments are conducted on Cityscapes, where a backbone pretrained on MS-COCO with strong random homographies over approximately planar images is fine-tuned with deliberately attenuated synthetic warps, as we found that reusing the aggressive COCO-style homographies on Cityscapes produced unrealistically large distortions. Within this controlled setting, we observe that adding semantic masking and sub-pixel refinement consistently improves stability on static structures and suppresses keypoints on dynamic or ambiguous regions.