Cold Region Building Inspection using UAV-based Three-dimensional Reconstruction
Bibliographic record
Abstract
Abstract. This paper presents a UAV-based workflow for the inspection of buildings in cold-region climates, focusing specifically on rooftop snow depth estimation and thermal reconstruction of building envelopes. Accurate measurement of rooftop snow depth is critical, as an excessive snow load can lead to structural damage or roof collapse, resulting in substantial economic losses and potential fatalities. Thermal reconstruction can indicate thermal bridging in the building envelope, a phenomenon that can significantly reduce heating efficiency, causing increased energy consumption and higher utility expenses. Case studies conducted at multiple locations demonstrate the efficacy of UAV photogrammetry in accurately measuring rooftop snow depth, validated using manual field measurements and LiDAR scanning. We also propose a method of joint RGB-thermal reconstruction, capable of producing models with a high degree of geometric and radiometric accuracy without the requirement of homography or 3D transformations for image pair alignment. Results validate the approach through field measurements and pixel-wise thermal model evaluation. The proposed methods provide efficient, accurate, and safe alternatives to traditional inspection practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".