Fossils constitute a significant part of Earth's biodiversity, holding considerable heritage value. With an aim of improving and enhancing dissemination of knowledge and by integrating augmented reality technologies, this paper presents an AR application to identify fossils in two fossil sites, that of Agia Marina (Aegina Island) and Rafina (Attiki peninsula). An application compatible to mobile devices that enables the user to identify the fossils in situ, promoting learning through interactive experiences. It offers virtual tours that transport users back in time to the paleo environments where these fossils were originally found. This application seeks to make science, and especially paleontology, accessible and engaging to a wide range of people. To achieve this, we employed advanced photogrammetry techniques. By utilizing these methods, we were able to create detailed and accurate 3D models of marine fossils and environments. This approach not only enhances the visual representation of these paleo-environments but also provides precise data for scientific analysis and educational purposes. The AFT project allows us to capture the intricacies of invertebrate species and their habitats, ensuring that their importance within the marine ecosystem is effectively communicated and appreciated by a wider audience.

Background: Monitoring external load in team sports is essential for performance optimization, injury prevention, and individualized training prescription. Although Local Positioning Systems (LPS) are widely used for indoor athlete tracking, they require wearable devices and specialized infrastructure. Recent advances in artificial intelligence and computer vision allow markerless athlete tracking; however, their validity for basketball remains insufficiently explored. Objective: To evaluate the validity of a deep-learning multi-camera computer-vision system for quantifying external-load variables in basketball compared with a commercial LPS. Methods: The framework integrated fisheye video acquisition, player detection, and pose estimation using YOLOv11x-Pose and player re-identification through ResNet-50 and FAISS similarity search. Positional data were transformed into real-world court coordinates to derive distance, acceleration, deceleration, player load, and average speed metrics. Outputs were compared with measurements obtained from Kinexon LPS. Results: Strong correlations were observed for total distance (r = 0.92), acceleration counts (r = 0.90), deceleration counts (r = 0.92), and player load (r = 0.81), while average speed showed a moderate-to-strong correlation (r = 0.66). ICC and Bland–Altman analyses indicated agreement between systems. Conclusions: The proposed computer-vision system demonstrated high agreement with LPS, supporting its use as a valid, non-invasive, and scalable solution for external load monitoring in basketball.