PRISMethaNet: A novel deep learning model for landfill methane detection using PRISMA satellite data
Bibliographic record
Abstract
Methane (CH4) is one of the most significant greenhouse gases responsible for about one-third of climate warming since preindustrial times, originating from various sources. Landfills are responsible for a large percentage of CH4 emissions, and population growth can boost these emissions. Therefore, it is vital to automate the process of CH4 monitoring over landfills. This study proposes a convolutional neural network (CNN) with an Atrous Spatial Pyramid Pooling (ASPP) mechanism, called PRISMethaNet, to automate the CH4 detection process using PRISMA satellite data in the 400–2500 nm spectral range. A total number of 41 PRISMA images from 17 landfill sites located in several countries, such as India, Nigeria, Mexico, Pakistan, Iran, and other regions, were used as our study areas. The PRISMethaNet model was trained using augmented data as the input, and plume masks were obtained from the matched filter (MF) algorithm. This novel proposed model successfully detected plumes with overall accuracy (OA), F1-score (F1), precision, and recall of 0.99, 0.96, 0.93, and 0.99, respectively, and quantification uncertainties ranging from 11 % to 58 %. An unboxing of the ASPP module using Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm demonstrated a strong relationship between larger dilation rates (DRs) and CH4 plume detectability. Importantly, the results highlighted that plume masks obtained by PRISMethaNet provided more accurate CH4 quantification rate compared to the statistical methods used in previous studies. In particular, the mean square error (MSE) for PRISMethaNet was approximately 1,102 kg/h, whereas the MSE for the commonly used statistical method was around 1,974 kg/h.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".