Parking spots are essential components, providing vital mobile resources for residents in a city. Accurate Global Positioning System (GPS) points of parking spots are the core data for subsequent applications,e.g., parking management, parking policy, and urban development. However, high-rise buildings tend to cause GPS points to drift from the actual locations of parking spots; besides, the standard lower-cost GPS equipment itself has a certain location error. Therefore, it is a non-trivial task to correct a few wrong GPS points from a large number of parking spots in an unsupervised approach. In this paper, motivated by the physical constraints of parking spots (i.e., parking spots are parallel to the sides of roads), we propose an unsupervised low-rank method to effectively rectify errors in GPS points and further align them to the parking spots in a unified framework. The proposed unconventional rectification and alignment method is simple and yet effective for any type of GPS point errors. Extensive experiments demonstrate the superiority of the proposed method to solve a practical problem. The data set and the code are publicly accessible at:https://github.com/pangjunbiao/ITS-Parking-spots-Dataset.

翻译：停车位是城市中为居民提供关键移动资源的重要基础设施。准确的停车位全球定位系统（GPS）点是后续应用（如停车管理、停车政策制定及城市发展规划）的核心数据。然而，高层建筑往往导致GPS点偏离停车位的实际位置；此外，标准低成本GPS设备本身也存在一定的定位误差。因此，在无监督条件下从大量停车位数据中校正少量错误GPS点是一项具有挑战性的任务。本文基于停车位的物理约束（即停车位通常平行于道路边缘），提出一种无监督低秩方法，在统一框架内有效校正GPS点误差并将其对齐至实际停车位。所提出的非常规校正与对齐方法简洁高效，适用于各类GPS点误差。大量实验验证了该方法在解决实际问题上的优越性。数据集与代码已公开于：https://github.com/pangjunbiao/ITS-Parking-spots-Dataset。