Parking violations in Berlin based on mobile mapping data (2019) / Parkverstöße in Berlin basierend auf Bildbefahrungsdaten (2019)

“Mobile mapping data” or “geospatial videos”, as a technology that combines GPS data with videos, were collected from the windshield of vehicles with Android Smartphones. Nearly 7,000 videos with an average length of 70 seconds were recorded in 2019. The smartphones collected sensor data (longitude and latitude, accuracy, speed and bearing) approximately every second during the video recording. Based on the geospatial videos, we manually identified and labeled about 10,000 parking violations in data with the help of an annotation tool. For this purpose, we defined six categorical variables (see PDF). Besides parking violations, we included street features like street category, type of bicycle infrastructure, and direction of parking spaces. An example for a street category is the collector street, which is an access street with primary residential use as well as individual shops and community facilities. Obviously, the labeling is a step that can (partly) be done automatically with image recognition in the future if the labeled data is used as a training dataset for a machine learning model. https://www.bmvi.de/SharedDocs/DE/Artikel/DG/mfund-projekte/parkright.html https://parkright.bliq.ai"移动测绘数据（Mobile mapping data）"与"地理空间视频（geospatial videos）"作为融合全球定位系统（GPS）数据与视频的技术载体，其采集自搭载安卓智能手机（Android Smartphones）的车辆挡风玻璃。2019年共录制近7000段视频，单段平均时长为70秒。在视频录制期间，智能手机约每秒采集一次传感器数据，涵盖经纬度、定位精度、行驶速度与方位角。 基于上述地理空间视频，研究人员借助标注工具（annotation tool）手动识别并标注了约10000条停车违规（parking violations）数据。为此共定义六类分类变量（categorical variables，详见PDF文档）。除停车违规类别外，数据集还纳入街道特征信息，包括街道类型、自行车基础设施类型以及停车位朝向。其中街道类型的示例为集散道路（collector street），即一类以住宅通行功能为主，同时配套沿街商铺与社区设施的通行道路。 显而易见，若将此类标注数据用作机器学习模型（machine learning model）的训练数据集，未来该标注环节可（部分）通过图像识别（image recognition）技术自动完成。 https://www.bmvi.de/SharedDocs/DE/Artikel/DG/mfund-projekte/parkright.html https://parkright.bliq.ai