A training dataset for machine learning-based prediction of window opening position in a naturally ventilated building
Bibliographic record
Abstract
Window operation is the main strategy used by building occupants to naturally ventilate buildings. However, common approaches to measure window operation for energy and comfort assessments are still technically complex or insufficient; typical window open/close sensors often provide only binary information about the opening state of a window, not the extent to which the window is open. This paper is the first outcome of a research project that seeks to use photo imagery and machine learning to predict the variable opening state of windows on a real multi-family residential passive house located in Vancouver, Canada. The employed windows are European-style in that they can be opened in tilt or turn mode. To eventually train the algorithm, a ground-truth dataset is constructed by manually changing the opening state of sixteen windows every minute over a 15-hour test period spanning three days and taking a photo of the windows at each instance, measuring the angle each time. This paper documents the first outcome of the overall project: the publication of the training dataset itself, with over 10,000+ images of a building fac¸ade taken, under variable-but-known window opening state, and under various light conditions. The paper presents the testing methodology undertaken for generation of the dataset and provides instructions for how to access the dataset. In the future, these images will be used to calibrate a machine learning model to estimate window opening/closing state of the tested building. The dataset can also be extended for semantic segmentation in support of other machine learning problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".