DEVELOPMENT OF 3D MARINE CADASTRE DATA MODEL BASED ON LAND ADMINISTRATION DOMAIN MODEL
Bibliographic record
Abstract
Abstract. A new version of the Land Administration Domain Model (LADM) has been discussed and is under further development in ISO/TC 211 on Geographic Information. One of the extending parts is where the model can accommodate complex and advanced marine properties and cadastral objects. Currently, the fundamentals part of this new version (LADM Edition II) has been examined by the committee, and a few elements need to be considered, especially for marine space georegulation. Based on the possibility of embedding LADM with marine cadastre as agreed by several researchers, the concept of marine cadastre data model within land administration context has been anticipated in many countries (e.g., Canada, Greece, Turkey, Australia, and Malaysia). Part of the research focused on constructing and developing the appropriate data models to manage marine spaces and resources most effectively. Several studies have attempted to establish a conceptual model for marine cadastre in Malaysia. However, there is still no acceptable marine data model. Thus, this paper proposed a marine data model for Malaysia based on the international standard, LADM. The approach, by definition, can be applied to the marine environment in terms of controlling and modelling a variety of rights, responsibilities, and restrictions. The Unified Modelling Language (UML) application was utilized to construct the conceptual and technical models via Enterprise Architect as part of the validation process. The data model was constructed within the marine's concept in Malaysia to meet international standards. The features of the data model were also discussed in the FIG workshop (9th LADM International Workshop 2021). The experiment on the data model also includes 3D visualization and simple query.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".