Bibliographic record
Abstract
With the threat of global warming, there is a need to implement sustainable solutions. Due to urbanization, forests and other natural resources that could have helped reduce carbon emissions are being depleted. Thus, cities, like Toronto, experience urban heat island effects, food shortages and increased carbon emissions. To help solve this, Green Roofs are encouraged to be implemented in Toronto to help reduce the mentioned issues. In addition, green roofs are difficult to maintain and COVID-19 making it more difficult for urban farmers to physically go and check the green roofs. Hence, this study proposes to use deep learning in detecting green roofs from satellite imagery. The approach uses NDVI values and Toronto Building data to obtain green roof locations. From here, satellite images were taken from map services such as Google Earth Pro and ArcGIS Pro. The images were preprocessed and fed into Faster R-CNN model to detect green roofs. Since this is a first step in creating a system that can monitor the Green Roof Food-Energy-Water nexus, in the future, the behaviour of Faster R-CNN with Toronto Roof Satellite Images was observed. It was shown that the Faster R-CNN provided a higher validation accuracy and higher confidence score than the Fast R-CNN. Additionally, it is suggested that the current Faster R-CNN model needs to be improved, to precisely detect green roofs in different image resolutions and dimensions.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".