Extraction of water bodies from Sentinel-2 imagery in the foothills of Nepal Himalaya
Bibliographic record
Abstract
This paper evaluates an integrated water body mapping method in sub Himalayan region of Nepal with optical images of Sentinel – 2 satellites of European Space Agency. The objectives of this study is to evaluating the integrated method of water body mapping with Sentinel – 2 data and to find the optimal mapping method in Sub Himalaya region. This method extracts the information on water bodies by combining image indices and near infrared band and used slope image to remove false results.. The study results indicate that difference of indices is more accurate to map the water bodies than single index method as it enhance the contrast between water bodies and other environmental features. On the basis of the accurately mapped water bodies of the study area, this research conclude that the multi spectral images from the Sentinel images can be ideal data source for water bodies monitoring with fine spatial and temporal resolution. Although smaller water bodies with high vegetation cover cannot be detected by this method, the integrated water body mapping method is suitable for the applications multi-spectral images in this field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".