Artificial Intelligence Associated Drones Solutions for Waste Disposal Management in the Process Industries
Bibliographic record
Abstract
Abstract The paper aims to provide an overview of "Waste Management Solution," an Artificial Intelligence computer vision solution that can detect the location, classify, and quantify waste on a geospatial map constructed by aerial images collected with drones. The objective is to demonstrate how drones with integrated AI solutions can drive efficiency, productivity, and innovation in industrial operations. The solution comprises drones collecting aerial image data and implementing cloud-based AI/Machine learning (ML) models to detect waste materials. By integrating drone technology, AI, and mapping techniques, the solution supports industrial organizations, government authorities, and environmental agencies in achieving their net-zero goals, aligned with the Saudi Green Initiative 2030, and aiming to create a cleaner environment. The solution offers a cost-effective method for processing industries facilities and the environmental and urban planning of smart cities by digitizing waste management practices, replacing time-consuming manual industrial waste inspections. The benefits of the solution are demonstrated through our experience of an actual project conducted with the project management office of a large oil and gas operating company.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".