Building Rooftop Extraction Using Machine Learning Algorithms for Solar Photovoltaic Potential Estimation
Why this work is in the frame
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Bibliographic record
Abstract
Green cities worldwide are converting to renewable clean energy from natural sources such as sunlight and wind due to the lack of traditional resources and the significant increase in environmental pollution. This paper presents an approach of two stages for photovoltaic (PV) potential estimation of solar panels mounted on buildings’ rooftops. The first stage is rooftop detection from satellite images using a series of image pre-processing algorithms, followed by applying machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). The second stage is the solar PV potential estimation using the PVWatts calculator, PVGIS, and ArcGIS. Satellite images for the B6 division of Madinaty City in Egypt were evaluated in this paper. The precision, recall, and F1-score of rooftop detection were 91.2%, 98.6%, and 94.7% from SVM, while those from NB were 86.6%, 98.3%, and 92.2%, respectively. About 290 rooftops were extracted, with a total area of 150,698 m2 and a relative root mean square error of 10.6%. The usable area of rooftops was utilized to estimate the annual PV potential of 21.1, 24.9, and 22.9 GWh/year from the PVWatts calculator, PVGIS, and ArcGIS, respectively. According to the estimated PV potential, replacing traditional energy sources reduced the amount of CO2 by an annual average value of 62%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it