COVMAP-2: Continuation of Comprehensive Conjoint GPS, Sensor and Video Data Analysisfor Next Generation of Smart Maps

在过去几年中，空间数据的可用性得到了迅速发展。这一发展的特点是大量用户的参与，他们经常使用智能手机和移动的设备，以生成和提供免费的地理信息（VGI）。虽然GPS和陀螺仪数据（例如，具有健身带）是常见的，但是通常以视频形式容易地收集的大量数据使得基于视频分析的方法非常苛刻。另一方面，只有视频允许全面的场景解释。在这个研究项目中，我们有兴趣结合GPS，陀螺仪和视频数据来分析骑自行车者和行人的道路和交通状况。我们的标准设置是一个智能手机连接到自行车，记录GPS坐标，视频，（在线）当地天气信息和时间。我们将（a）使用GPS数据整合到地图中，（B）本地速度和陀螺仪数据以及传感器数据的变化将用于识别骑自行车期间的重要情况，以及（c）视频数据将用于了解导致例如骑行延迟的这些重要情况。然后，分析的数据可以用于地图增强和路径推荐，而且还用于识别对城市规划和避免事故很重要的不清楚的道路标记。除了收集以人为中心的应用程序的实时和事件触发数据的经验外，基础可以很容易地扩展到灾害后的交通管理，地形数据集的质量控制或使用额外的传感器信息进行环境和健康相关的数据分析。

During the last years the availability of spatial data has rapidly developed. Characteristic for this development is the involvement of large number of users, who frequently use smart phones and mobile devices, to generate and make freely available Volunteered Geographic Information (VGI). Whereas GPS and gyroscope data (e.g. with fitness-straps) are common, the huge amount of data, which are easily and typically collected in the form of videos, make video analysis based methods very demanding. On the other hand, only videos allow for a comprehensive scene interpretation. In this research project, we are interested in combining GPS, gyroscope and video data to analyze road and traffic situations for cyclists and pedestrians. Our standard setting is a smart phone attached to a bicycle, which records the GPS coordinates, videos, (online) local weather information and time. We will (a) use the GPS-data for integration in a map, (b) local velocities and gyroscope data, as well as variations in the sensor data, will be used to identify important situations during a bicycle ride, and (c) video data will be used to understand these important situation which causes e.g. a delay in the ride.The analyzed data can then be used for map enhancement and path recommendation, but also for the identification of unclear road marks which is important for city planning and accident avoidance. Besides collecting experience with real-time and event triggered data with a focus on a human-centered application, the foundations can easily be extended towards traffic management after hazards, quality control of topographic datasets or environmental and health-related data analysis using additional sensor information.