Large-Scale LoD2 Building Modeling using Deep Multimodal Feature Fusion
Bibliographic record
Abstract
In today’s rapidly urbanizing world, accurate 3D city models are crucial for sustainable urban management. The existing technology for 3D city modeling still relies on an extensive amount of manual work and the provided solutions may vary depending on the urban structure of different places. According to the CityGML3 standard of 3D city modeling, in LoD2, the roof structures need to be modeled which is a challenging task due to the complexity and diversity of roof types. While high-resolution images can be utilized to classify roof types, they have difficulties in areas with poor contrast or shadows. This study proposes a deep learning approach that combines RGB optical and height information of buildings to improve the accuracy of roof type classification and automatically generate a 3D city model. The proposed methodology is divided into two phases: (1) classifying roof types into the nine most popular roof types in New Brunswick, Canada, using a multimodal feature fusion network, and (2) generating a large-scale LoD2 3D city model using a model-driven approach. The evaluation results show an overall accuracy of 97.58% and a Kappa coefficient of 0.9705 for the classification phase and an RMSE of 1.03 (m) for the 3D modeling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".