Field data collection and geospatial map generation are critical aspects in different fields such as road asset management, urban planning, and geospatial applications. However, one of the primary impediments to data collection is the availability of spatial and attribute data. This issue is aggravated by the high cost of conventional data collection and data processing methods and by the lack of geospatial data collection policies. This study proposes an inexpensive approach that enables real-time field data observation and geospatial data generation from video streams connected to a laptop and positioning sensors using deep learning technology. This proposed method was evaluated via an application called “DeepAutoMapping”, which was built on top of Python, then underwent through two different evaluation scenarios. The results demonstrated that the proposed approach is quick, easy to use and that it provides a high detection accuracy and an acceptable positioning accuracy in the outdoor environment. The proposed solution may also be considered as a pipeline for efficient and economical method of geospatial data collection and auto-map generation in the future.

现场数据收集和地理空间地图生成是道路资产管理，城市规划和地理空间应用等不同领域中的关键方面。但是，数据收集的主要障碍之一是空间和属性数据的可用性。常规数据收集和数据处理方法的高成本以及缺乏地理空间数据收集策略会加剧该问题。这项研究提出了一种廉价的方法，该方法可以使用深度学习技术从连接到笔记本电脑和定位传感器的视频流中进行实时现场数据观察和地理空间数据生成。通过一个名为“ DeepAutoMapping”的应用程序对该提议的方法进行了评估，该应用程序基于Python构建，然后经历了两种不同的评估方案。结果表明，所提出的方法快速，易于使用，并且在室外环境中具有很高的检测精度和可接受的定位精度。所提出的解决方案也可以被认为是未来高效，经济的地理空间数据收集和自动地图生成方法的管道。