Generating LoD2 City Models Using a Hybrid-Driven Approach: A Case Study for New Brunswick Urban Environment
Bibliographic record
Abstract
Today 55% of the world's population lives in urban areas, a proportion that is expected to increase to 68% by 2050 (UN, 2018). 3D city models can be used to prepare for the future city, enabling informed analysis and sustainable development. Based on the Open Geospatial Consortium (OGC) standard, i.e. cityGML, 3D city models can be produced in different levels of detail (LOD). CityGML-3 introduces five predefined LODs (LOD0-4), with LOD0 being a building footprint and LOD4 being a realistic model representing the exterior and interior of the buildings. Currently, LOD0 and LOD1 are available for most cities in developed countries while LOD2+ are superior for informed analysis in different applications such as disaster management and insurance. However, with the current status of knowledge and technology, the production, storage and maintenance of such models are very time-consuming and expensive. This paper presents an initial study for 3D city model generation with a focus on the urban structure of New Brunswick, Canada, which is an introductory part of a larger project for 3D city modelling and maintenance in Canada. This paper intended to explore existing off-the-shelf 3D city modelling products and check their accuracies. Furthermore, inspired by existing literature, we proposed a decision-tree-based methodology for LoD2 3D city model generation, which follows a combination of data-driven and model-driven approaches, i.e. a hybrid approach. We tested the quality of the final 3D models using different metrics such as overall accuracy, Kappa Coefficient, Root Mean Square Error (RMSE) and slope difference. Besides, we compared our results to two off-the-shelf products, namely Schematic Local Government City Engine LOD2 (SLGCE) modelling and OpenStreet Map City Engine (OSMCE) LOD2 modelling. The results showed that the proposed hybrid approach achieved higher accuracies using the mentioned metrics. This paper also discusses the pros and cons of the proposed method and offers insights for improving the results even further.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".