This paper describes the scientific achievements of a collaboration between a research group and the waste management division of a company. While these results might be the basis for several practical or commercial developments, we here focus on a novel scientific contribution: a methodology to automatically generate geo-located waste container maps. It is based on the use of Computer Vision algorithms to detect waste containers and identify their geographic location and dimensions. Algorithms analyze a video sequence and provide an automatic discrimination between images with and without containers. More precisely, two state-of-the-art object detectors based on deep learning techniques have been selected for testing, according to their performance and to their adaptability to an on-board real-time environment: EfficientDet and YOLOv5. Experimental results indicate that the proposed visual model for waste container detection is able to effectively operate with consistent performance disregarding the container type (organic waste, plastic, glass and paper recycling,…) and the city layout, which has been assessed by evaluating it on eleven different Spanish cities that vary in terms of size, climate, urban layout and containers' appearance.

Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID dataset.

This work presents an extended version of the work previously introduced in the Urban Elements ReID Challenge 2025. The competition was conceived as a contribution to the development of smart cities, focusing on the re-identification of urban elements such as rubbish bins, waste containers and crosswalks. The challenge provides a standardized evaluation framework and promotes the development of algorithms capable of re-identifying urban objects across image sequences collected over several months along the same route. To support fair comparison and reproducibility, a dataset recorded in a real urban environment was released, capturing long-term variations in viewpoint, illumination and season. Beyond describing the competition setup, the new contributions of this paper focus on the detailed challenge analysis: it examines the approaches of the participants, compares their performance on the public and private leaderboards, analyzes challenge statistics and reports class-wise results. Together, these studies provide a comprehensive view of the competition outcomes and provide helpful observations on current trends and limitations, showing future research lines.