Diagnosing Bottlenecks in GeoAI-Ready UAV Imagery Reuse for AI-Enabled Urban and Landscape Systems

Human-oriented smart cities and landscape systems increasingly depend on reusable, high-resolution geospatial observations, yet unmanned aerial vehicle (UAV) imagery is commonly acquired for a single task and retained by separate organizations. This study diagnoses the resulting reuse bottlenecks using an improved combinatorial weighting multi-criteria decision method (ICW-MCDM). Seven experts assessed four dimensions and 17 criteria. Data Rights (0.0912), Data Security (0.0823), Share Policy (0.0819), Data Description (0.0804), and Incentives (0.0706) received the highest integrated weights. A transparent raw-score comparator recovered the same five-item set, while bootstrap and leave-one-out checks supported a governance-oriented leading set with panel dependence for some criteria. After C1–C6 were excluded, Data Description, Reliable Data, Access Permissions, Service Facilities, Search and Discovery, and Data Citation and Provenance became the leading post-entry requirements. By 11 August 2026, a national directory platform had recorded 336,753 visits, fewer than ten formal applications, and two completed university research deliveries. The cases demonstrate small-scale matching, cross-institutional aggregation, and controlled delivery, but not general platform effectiveness or downstream GeoAI outcomes. The study separates governance entry from technical readiness and identifies governance and technical prerequisites for GeoAI-ready UAV data infrastructure.