A Bottom-Up Approach Integrating Computer Vision with Material Flow Analysis to Estimate the Recycling Potential of Distributed Solar Panels Using Satellite Imagery
Bibliographic record
Abstract
The rapid deployment of solar photovoltaic (PV) systems has created a growing challenge in managing end-of-life panels. While many studies project future recycling potential, they are often limited by the lack of data on existing distributed PV installations. To address this need, we developed SolarScope, an open-source model that integrates computer vision (CV) with dynamic material flow analysis (dMFA) to automatically identify distributed PV panel areas and evaluate the urban mining potential. By leveraging satellite imagery and Vision Transformer (ViT) models, SolarScope achieves an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.93 for classification and a Dice Similarity Coefficient (Dice) score of 0.90 for segmenting distributed PV installations. A case study in Kamakura, Japan, demonstrates the model's transferability and its ability to support material recovery assessments at fine spatial scales. We present a methodological framework that combines CV with dMFA to bottom-up estimate the regional material stock and recycling potential of distributed PV systems, providing a scalable solution to overcome data limitations in conventional material flow analysis and contributing to circular economy advancement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".