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Aerial-ground person re-identification (Re-ID) presents unique challenges incomputer vision, stemming from the distinct differences in viewpoints, poses,and resolutions between high-altitude aerial and ground-based cameras. Existingresearch predominantly focuses on ground-to-ground matching, with aerialmatching less explored due to a dearth of comprehensive datasets. To addressthis, we introduce AG-ReID.v2, a dataset specifically designed for person Re-IDin mixed aerial and ground scenarios. This dataset comprises 100,502 images of1,615 unique individuals, each annotated with matching IDs and 15 softattribute labels. Data were collected from diverse perspectives using a UAV,stationary CCTV, and smart glasses-integrated camera, providing a rich varietyof intra-identity variations. Additionally, we have developed an explainableattention network tailored for this dataset. This network features athree-stream architecture that efficiently processes pairwise image distances,emphasizes key top-down features, and adapts to variations in appearance due toaltitude differences. Comparative evaluations demonstrate the superiority ofour approach over existing baselines. We plan to release the dataset andalgorithm source code publicly, aiming to advance research in this specializedfield of computer vision. For access, please visithttps://github.com/huynguyen792/AG-ReID.v2.

AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification