Land registration is a foundational public service linking people, parcels, and legally recognized rights. In Nepal, legacy paper archives, inconsistent document formats, and mismatches between cadastral maps and ground conditions increase transaction time and weaken land governance. This paper proposes a Nepal-relevant, modular AI framework aligned with fit-for-purpose land administration principles: affordability, participatory verification, incremental upgrading, and clear accountability. The framework integrates computer vision for boundary extraction, document AI for record processing, and anomaly detection for internal quality control - all within a human-in-the-loop design that preserves legal accountability.
Nepal's land administration faces a classic tension between precision and coverage. Conventional cadastral approaches - high-accuracy surveys, intensive adjudication, engineered databases - require time and capacity difficult to sustain at national scale. Where coverage is incomplete, informal transactions increase, disputes persist, and the registry loses legitimacy.
The operational reality includes high service pressure at land revenue offices, legacy paper archives, inconsistent document formats across regions, and growing mismatches between cadastral maps and ground conditions in peri-urban areas. Post-disaster (earthquake, landslide), the demand for property documentation spikes precisely when the system is under stress.
The framework organizes AI into five bounded modules, each solving a specific problem and producing outputs that can be validated independently. This modular approach reduces operational risk and simplifies adoption in government settings.
The framework is conceptually grounded in the Land Administration Domain Model (LADM · ISO 19152) - the international standard for representing relationships between people, parcels, and rights. AI modules produce candidate evidence that can be inserted into LADM-aligned database fields only after human verification, preserving legal meaning.
Four categories of data materials are identified: spatial imagery (satellite, aerial, UAV orthophotos), cadastral and base-map layers (parcel polygons, control points, admin boundaries), documents (ownership certificates, deeds, mutation records), and transaction logs (mutation events, edit histories).
| Risk | How it Appears | Control |
|---|---|---|
| Boundary Invisibility | AI misses socially defined boundaries, produces misleading candidate lines | Participatory verification; treat outputs as candidates only |
| Extraction Errors | Wrong party names or parcel IDs extracted; rework increases | Human confirmation gate; source region evidence; continuous monitoring |
| Bias & Exclusion | Lower performance in informal settlements or minority language contexts | Diverse validation sets; fairness audits; complaint channels |
| Privacy Breach | Unauthorized access to personal and property data | Role-based access; encryption; data minimization policy |
| Automation Overreach | Staff accept AI outputs without review; legal harm embedded | Mandatory review gates; training; reversible correction procedures |
Nepal's land administration operates in a mixed digital–paper environment. Centralized web-based transaction systems (LRIMS) already exist, providing integration points for AI as assistive services - without replacing legal decision-making. Documents exist in Nepali script and mixed languages, requiring training data and validation procedures aligned with local forms.
The recommended adoption sequence: 1) Document AI for triage and indexing (lowest risk, highest return). 2) Imagery-based mismatch detection in selected municipalities. 3) Entity resolution and anomaly support as internal QA tools. All stages require mandatory verification gates and staff training.