Waterloo Building Dataset: A city-scale vector building dataset for mapping building footprints using aerial orthoimagery
Bibliographic record
Abstract
Automated building footprint extraction is an important area of research in remote sensing with numerous civil and environmental applications. In recent years, deep learning methods, when trained on appropriate datasets, have far surpassed classical algorithms. In this paper, we present the Waterloo Building Dataset for building footprint extraction from very-high-spatial-resolution aerial orthoimagery. Our dataset covers the Kitchener-Waterloo area in Ontario, Canada, contains 117,000 manually labelled buildings, and extends over an area of 205.8 km2. At a spatial resolution of 12 cm, it is the highest resolution publicly available building footprint extraction dataset in North America. We provide extensive benchmarks of commonly used deep learning architectures trained on our dataset which can be used as baseline for future models. We also identify a key challenge in aerial orthoimagery building footprint extraction which we hope can be addressed in future research.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".