Waste management and maintenance of urban infrastructure are central challenge for modern smart cities. Image-based re-identification systems can help to automate condition monitoring and to build intelligent solutions. However, most existing image-based re-identification research targets persons and vehicles, leaving other objects such as urbane elements largely unexplored. To close this gap, we adapt a state-of-the-art re-identification model to the Urban Elements ReID Challenge 2025, which encompasses infrastructure elements like containers, crosswalks, and rubbish bins. The introduced model (i) fuses intermediate backbone features to enrich global embeddings with fine-grained, low-level cues, and (ii) incorporates view-specific embeddings that exploit the direction of travel during image recording to enhance robustness to viewpoint changes. Combined with a post-hoc re-ranking method, our system achieves a mAP of 28.1% and ranks second on the public leaderboard.
Andreas Specker (Sun,) studied this question.