Re-(De)fined Level of Detail for Urban Elements: Integrating Geometric and Attribute Data
Bibliographic record
Abstract
The level of detail (LOD) differentiates multi-scale representations of virtual 3D city models; however, the LOD tends to relay primarily the geometric details of buildings. When the LOD extends to other datasets, such as vegetation, transportation, terrain, water bodies, and city furniture, their LODs are not as clearly defined. Despite the general acceptance of this categorization, existing LOD formats also neglect non-geometric attributes. Integrating geometric and attribute data enables geometrically accurate and data-rich 3D models, ensuring that representations are as accurate as possible and that analyses contain as much information as possible. This paper proposes a family of LOD definitions considering both geometric and attribute data based on the geometric complexity and difficulty of obtaining, archiving, processing, and distributing the data. These definitions are intended to apply to all datasets by determining divisions in the LOD typically experienced across urban 3D model elements and their associated datasets, including buildings, vegetation, roads, relief, water bodies, and city furniture. Universally applicable definitions for datasets allow individuals to recreate studies or representations of 3D models to ensure the relevant data are present. These definitions also assist data providers in evaluating their data infrastructure and further strategizing and prioritizing updates or upgrades.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".