ARCHITECTURAL HERITAGE VISUALIZATION USING INTERACTIVE TECHNOLOGIES
Bibliographic record
Abstract
Abstract. With the increased exposure to tourists, historical monuments are at an ever-growing risk of disappearing. Building Information Modelling (BIM) offers a process of digitally documenting of all the features that are made or incorporated into the building over its life-span, thus affords unique opportunities for information preservation. BIM of historical buildings are called Historical Building Information Models (HBIM). This involves documenting a building in detail throughout its history. Geomatics professionals have the potential to play a major role in this area as they are often the first professionals involved on construction development sites for many Architectural, Engineering, and Construction (AEC) projects. In this work, we discuss how to establish an architectural database of a heritage site, digitally reconstruct, preserve and then interact with it through an immersive environment that leverages BIM for exploring historic buildings. The reconstructed heritage site under investigation was constructed in the early 15th century. In our proposed approach, the site selection was based on many factors such as architectural value, size, and accessibility. The 3D model is extracted from the original collected and integrated data (Image-based, range-based, CAD modelling, and land survey methods), after which the elements of the 3D objects are identified by creating a database using the BIM software platform (Autodesk Revit). The use of modern and widely accessible game engine technology (Unity3D) is explored, allowing the user to fully embed and interact with the scene using handheld devices. The details of implementing an integrated pipeline between HBIM, GIS and augmented and virtual reality (AVR) tools and the findings of the work are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".