Abstract: With the rapid development of the low-altitude economy, the construction of low-altitude transportation infrastructure has progressed from conceptual exploration to scaled practice. The digital air-route network not only guides the construction of facility and air-internet networks, but also provides the service network with followable routes, making it a priority task in building low-altitude transportation infrastructure. However, the existing methods for constructing digital air-route networks insufficiently consider risk quantification, lack structured topology, and omit essential route attributes. In addition, they have not clarified the required types and geometric accuracies of geographic and constraint elements in low-altitude environments. Therefore, it is necessary to further improve relevant methodologies to better guide the construction and application of digital air-route networks for the low-altitude economy. To address these issues, this study begins with the interaction mechanism between unmanned aerial vehicles (UAVs) and their geographic constraint environments. It identifies the categories of geographic and constraint elements required for digital air-route network construction and specifies the geometric accuracy requirements for geographic elements. The feasibility and adequacy of spaceborne remote sensing techniques for acquiring these elements are analyzed. Based on these findings, a construction method for digital air-route networks is proposed, integrating geographic and constraint information while jointly optimizing topological structure and risk. Field experiments are conducted in Anyang to verify the feasibility of this method, including validation of the spaceborne geographic information base, meteorological constraints, the digital air-route network itself, and the communication and positioning quality along the routes. Results show that spaceborne remote sensing data achieve a DSM vertical accuracy better than 2m, a building white model accuracy of 3.83m, an overall obstacle recognition accuracy of 80.77%, and a land cover classification accuracy of 79.5%. These results collectively meet the meter level geometric and surface-attribute resolution requirements for digital air-route network construction. Compared with manually designed routes, the air-route network generated with this method reduces route length by 7.6%, cruise time by 12.6%, and the proportion of high-risk segments by 7.6%, while increasing the nonlinearity coefficient by 8.2%. Compared with pilot-planned ad-hoc routes, route length decreases by 4.2%, cruise time by 3.4%, and the nonlinearity coefficient improves by 18.5%. Overall, the proposed method effectively improves airspace utilization, reduces flight risk, and enhances flight efficiency, fulfilling the operational requirements for UAVs to fly, fly safely, and fly efficiently in large-scale low-altitude operations. 

Key words: low-altitude economy, spaceborne remote sensing, geographic information base, digital air-route network, flight test validation