Georeferencing Building Information Models for BIM/GIS Integration: A Review of Methods and Tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rise of urban digital twins and smart cities, the integration of building information modeling (BIM) and geospatial information systems (GISs) have captured the interest of researchers. Although significant advancements have been achieved in this field, challenges persist in the georeferencing of BIM models, which is one of the fundamental challenges in integrating BIM and GIS models. These challenges stem from dissimilarities between the BIM and GIS domains, including different georeferencing definitions, different coordinate systems utilization, and a lack of correspondence between the engineering system of BIM and the project’s geographical location. This review critically examines the significance of georeferencing within this integration, outlines and compares various methods for georeferencing BIM data in detail, and surveys existing software tools that facilitate this process. The findings underscore the need for increased attention to georeferencing issues from both domains, aiming to enhance the seamless integration of BIM and GIS.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.001 | 0.000 |
| 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 it