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.

Exchange-listed medical data products create information and pricing asymmetry because sellers, buyers, and governance reviewers do not observe the same product boundaries, scenario permissions, processing depth, or compliance costs. This study proposes SM-DPF, an auditable pricing reference framework that makes these asymmetric conditions explicit through a reproducible three-layer chain: a public investment anchor, a locked structural scoring model, and a proposed future market learning governance interface that is not implemented or evaluated in the present study. Using 23 de-identified transaction-descriptive records from a single exchange context across insurance claims, model pretraining, and pharmaceutical R&D, the pricing reference outputs y_0,i show in-sample diagnostic consistency with de-identified transaction prices y_i, with an overall mean absolute percentage error of 4.82% and median absolute percentage error of 4.55%. The same 23 records informed the initial scenario-response calibration and the subsequent diagnostics; the reported errors and correlations are, therefore, in-sample diagnostics only. SM-DPF is a governance-oriented reference tool for transparent listing decisions and audit replay with no transaction price prediction claim.