MetaPKLot – new challenges and protocols for vision-based parking lot management

We introduce MetaPKLot, a large-scale, harmonized dataset designed for vision-based parking lot management. It supports three key tasks: parking spot occupancy recognition, dwell time estimation, and parking spot extraction. MetaPKLot builds upon existing datasets, namely PKLot, CNRPark-EXT, and PLds, by adding over 1.3 million new annotations and revising approximately 900,000 existing ones, making it one of the largest publicly available human-annotated parking datasets to date. The new annotations include occupancy labels, segmentation masks, vehicle identifiers, and timestamps that enable dwell time computation. We also define standardized challenges and experimental protocols for each task, emphasizing not only predictive accuracy but also computational efficiency and generalization across unseen parking lots. Furthermore, we provide baseline methods that establish strong reference points for future research. By combining new annotations, standardized protocols, and baseline implementations, MetaPKLot serves as a comprehensive benchmark for developing and evaluating vision-based parking management systems under realistic, reproducible conditions.