Towards an AI-Ready LADM Framework for GeoAI-Enhanced Land Administration: Semantic Mediation, Uncertainty Representation and Legal Validation

The increasing availability of geospatial data and the rapid development of geospatial artificial intelligence (GeoAI) create new opportunities for modernizing cadastral and land administration systems. However, integrating probabilistic GeoAI outputs into systems structured according to the Land Administration Domain Model (LADM) remains conceptually, semantically and institutionally challenging. ISO 19152 provides standardized structures for land-administration objects, sources and lifecycle information, while ISO 19157 supports the description and evaluation of geographic data quality. A distinct issue nevertheless arises before these mechanisms can support authoritative cadastral information: the status and institutional treatment of model-dependent spatial observations. Using a conceptual framework-development approach based on a critical and integrative literature synthesis, this article proposes an AI-ready LADM framework organized into six interdependent layers: geospatial data acquisition, GeoAI inference, uncertainty representation, semantic mediation, candidate integration, and legal validation with feedback. The framework introduces external, non-authoritative candidate-evidence constructs that preserve source provenance, model information, uncertainty, possible LADM relevance and review history before any institutionally authorized cadastral action. It translates these constructs into a proposed procedural structure comprising source classification, uncertainty and risk assessment, conceptual semantic-mapping rules, validation procedures, audit trails and institutional roles. The contribution is domain-specific rather than a general claim about human oversight of AI. It distinguishes technical detection, candidate cadastral relevance and legally recognized cadastral status, while treating semantic mediation as one component of the broader process of epistemic translation. Scenario-based analysis illustrates the framework’s internal logic, but its effectiveness, usability and legal–institutional feasibility remain subject to subsequent expert assessment and jurisdiction-specific empirical evaluation.