Site Selection Method for UAV Full-Domain Inspection Nests under Multi-factor Constraints: A Case Study of Guang'an City

Abstract Unmanned Aerial Vehicles (UAVs) are widely used for automated urban inspection, and the spatial layout of UAV nests directly affects inspection costs. To achieve efficient and stable city-scale UAV nest network construction, this study takes Guang'an City as an example. Based on multi-source data including terrain relief, temperature, and wind speed, a multi-factor environmental suitability evaluation system is established. A siting method based on "regional zoning + facility location model + heuristic exchange" is proposed. First, spatial zoning is used to reduce large-scale computational complexity. Then, an initial UAV nest layout is obtained by minimizing inspection distance costs. Finally, a heuristic exchange algorithm is applied to suppress repeated coverage, improve spatial balance, and minimize the number of constructions, ultimately forming an optimized spatial layout of UAV nests. The research results show that: ①Terrain is the dominant factor in UAV nest construction, with environmental suitability showing significant terrain differentiation patterns. ②The scalable spatial optimization siting method framework, combining zonal p-median solutions with heuristic exchange, effectively improves coverage and siting stability. ③The UAV nests for citywide inspection in Guang'an City achieve a balance among "coverage, cost, and redundancy." This method can balance coverage efficiency and facility quantity in large-scale real-world scenarios. The research findings provide theoretical reference and practical support for urban governance.