A REVIEW OF 3D GIS FOR USE IN CREATING VIRTUAL HISTORIC DUBLIN
Bibliographic record
Abstract
Abstract. This paper illustrates how BIM integration with GIS is approached as part of the workflow in creating Virtual Historic Dublin. A design for a WEB based interactive 3D model of historic buildings and centres in Dublin City (Virtual Historic Dublin City) paralleling smart city initiates is now under construction and led by the National Monuments at the Office of Public Works in Ireland. The aim is to facilitate the conservation and maintenance of historic infrastructure and fabric and the dissemination of knowledge for education and cultural tourism using an extensive Historic Building Information Model. Remote sensing data is now processed with greater ease to create 3D intelligent models in Historic BIM. While the use of remote sensing, HBIM and game engine platforms are the main applications used at present, 3D GIS has potential to form part of the workflow for developing the Virtual Historic City. 2D GIS is now being replaced by 3D spatial data allowing more complex analysis to be carried out, 3D GIS can define and depict buildings, urban rural centres in relation to their geometry topological, semantic and visualisation properties. The addition of semantic attributes allows complex analysis and 3D spatial queries for modelling city and urban elements. This analysis includes fabric and structural elements of buildings, relief, vegetation, transportation, water bodies, city furniture and land use.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".