Application of Dempster-Schafer theory for predicting ephemeral gullies with remote sensing data
Bibliographic record
Abstract
Mapping Ephemeral Gullies (EGs) is essential for enhancing the management and conservation of natural and agricultural resources. This study aimed to devise a novel approach utilizing the Dempster-Shafer (D-S) theory to achieve more accurate EGs mapping. To accomplish this, a high-resolution satellite image and ground data collected during a field visit to the Niagara region in Canada were utilized. Firstly, spectral features that effectively identify EGs were extracted from the satellite imagery. Subsequently, three machine learning classifiers including the artificial neural network (ANN), support vector machine (SVM), and random forest (RF) were employed to generate the EGs map. Finally, the D-S theory was applied to integrate the outcomes from these classifiers, aiming for a more precise EGs map. The results highlighted that the most significant variable importance was attributed to the Normalized Near-Infrared (NIR) (18%) and Soil line (15%). The average producer and user accuracies for the EGs and non-EGs classes across the three classifiers were 0.53 and 0.67, and 0.97 and 0.95, respectively. Incorporating the D-S theory enhanced these accuracy values by 0.15 and 0.19 for EGs and 0.02 and 0.02 for non-EGs. Furthermore, the overall accuracies for the EGs maps generated by the ANN, SVM, RF, and D-S theory models were 94%, 93%, 95%, and 97%, respectively. The results of this study showed that D-S theory is useful for improving the EGs mapping using remote sensing data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".