Rethinking UX in 3D Cadastral systems for inclusive land governance
Bibliographic record
Abstract
This article examines how User Experience (UX) is conceptualized and evaluated in the context of 3D cadastral systems, and advocates for a more holistic and inclusive approach to their design. Although 3D cadastres are often promoted as more accessible and engaging for non-expert users, little attention has been paid to how users - whatever their level of expertise - actually experience and interact with these systems. Our literature review reveals that UX is frequently reduced to usability, overlooking non-instrumental dimensions such as aesthetics, symbolism, and user motivations, factors that are essential to inclusive land governance. Moreover, evaluations of 3D cadastral prototypes rarely involve non-expert participants, and interdisciplinary collaborations remain sparse. To bridge these gaps, we propose reframing UX in 3D cadastres around three pillars: the integration of broader experience dimensions, the inclusion of diverse user groups through participatory methods, and the promotion of interdisciplinary design practices. We argue that institutions like the International Federation of Surveyors (FIG) could play a pivotal role in this transformation by promoting cross-disciplinary dialogue and supporting structured citizen engagement in cadastral research and development. Reimagining UX in this way can not only improve system performance and adoption but also enhance the social legitimacy and accessibility of future cadastral infrastructures.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".