Evaluation of open geospatial data quality and impact of public transportation on flight planning to develop a dynamic drone geofencing system for an urban environment

Kurzfassung

Over the past few years, the use of drones for real-time applications has been steadily increasing. Due to the rapid practice of drones, aviation authorities are being urged to regulate drone operations. However, the Unmanned aircraft system Traffic Management (UTM) is still in the implementation stage and unavailable for all users. This research focuses on developing a dynamic geofencing system with open geospatial data and open-source software and analyses the impacts of public transport on drone flight plans in an urban environment. Five input sources, namely OpenStreetMap (OSM), INSPIRE geodata, Deutsche Bahn (DB) open data, Open GeoData Braunschweig, and Open GeoData Niedersachsen, were examined to find out the quality of geographical features. Rank-based metrics and suitability analysis were conducted, considering four metrics, namely, metadata, accuracy, completeness, and complexity. The regulation of the European Union Aviation Safety Agency (EASA) and German air navigation service provider (DFS) was applied to map the restricted areas for the "Specific" drone category using geospatial technology. As a quality result, OSM was the most suitable input source for the geographical features of restricted airspaces, protected areas, energy, highway, and tram networks. Likewise, appropriate features were acquired for the railway's network and building footprints from the Deutsche Bahn open data portal and Open geodata Niedersachsen, respectively. Furthermore, the 2D and 3D geofences have been enabled to address the control uncertainties in low-altitude airspace and perimeter issues. The dynamic geofences have been implemented with 45 simulated drones. The SUMO (Simulation of Urban MObility) software is used to simulate the road traffic vehicles consisting of 2000 cars, 4000 trucks, 450 motorcycles, 500 bicycles. Specifically, the public transport included the six tram lines, 90+ trains, and buses retrieved from the OSM public transportation schedule. The dynamic drone geofencing system with cooperative intelligent transport has been examined for secure flight planning. In Braunschweig's urban environment, drone flight planning has been tested with a conceptual transport geofencing framework and Local Dynamic Map (LDM) technology. The current work implemented Dijkstra's shortest flight planning algorithm to determine the optimal routes for drones. The drone flying distance and time comparison were predicted and visualized for the normal (without geofence) and dynamic geofencing environment. With 20 shortest routes, the flight planning algorithm detected a difference in flying distance of 939.35 meters and flying time of 37.57 seconds between normal and dynamic geofencing environments. This investigation was practical to form the central traffic system with drones. The spatial-temporal analysis was carried out to derive the impacts of transport geofences on drone flight planning. The implemented traffic data processing algorithm was used to determine the location of impact zones and traffic intensity ratio between 240 drone routes and transport vehicles. The predicted impacts of public transport on drone flight planning helps to understand geofence rules and consequences of drone operation in the urban environment. This research framework can be useful to identify the future challenges in UTM and is also applicable to other cities.