Nowadays, it is practically impossible to find a public place, where a person or object would not be under constant camera observation (mainly used to monitor traffic and to increase safety). Interpretation of such repositories, due to the huge amount of data, is not an easy task. Thus there is a need to introduce an intelligent processing systems that, through algorithmic image analysis, are able to detect e.g. situations worth of attention. The article presents methods of data acquisition from cameras mounted on unmanned aerial vehicles (UAV) and their further analysis, which may be used to improve urban transportation systems and its sustainability. The worked out data concern the situation of urban transport in points of intersection of national and local roads. The analysis of the road traffic concerning local road restoration, causing road closure was achieved using image processing and analysis algorithms. The data was obtained from unmanned aerial vehicle flights in critical city locations. Result of this paper may be used in the future together with the data from existing intelligent transportation systems (a fusion of such data will be needed). The application of used methods will allow to extend and support the existing approaches to manage public and freight transport in cities.

如今，几乎不可能找到一个人或物体不会受到持续摄像头观察的公共场所（主要用于监控交通和提高安全性）。由于数据量巨大，解释此类存储库并非易事。因此，需要引入智能处理系统，该系统通过算法图像分析能够检测例如值得关注的情况。本文介绍了从安装在无人机 (UAV) 上的摄像头获取数据的方法及其进一步分析，这些方法可用于改善城市交通系统及其可持续性。计算出的数据涉及国道与地方道路交汇点的城市交通状况。涉及局部道路修复的道路交通分析，导致道路封闭是使用图像处理和分析算法实现的。这些数据是从无人机在关键城市位置的飞行中获得的。本文的结果将来可能会与现有智能交通系统的数据一起使用（需要这些数据的融合）。所使用方法的应用将允许扩展和支持管理城市公共和货运的现有方法。