Flood mapping of Danube River at Romania using multi-date ERS2-SAR data
Bibliographic record
Abstract
The objective of this study is to explore some flood map classification techniques to identify flooded areas of Romania due to the 2006 Danube River flooding events, to improve the accuracy of flood mapping using multi-date ERS-2 SAR data. Among the 7 SAR images analyzed for the same study site locate around Bistret of Romania, several likely represent “dry” and several “wet” conditions, and the latter were mainly due to the major Danube flooding event of 2006. The images were classified into (1) permanent water (Danube River and lakes), (2) flooded area, and (3) dry land, using single image supervised classification, frequency-based contextual classification, principal component analysis, and an orthogonal, 2-dimensional wavelet transform of Mallat. The flooded areas delineated from the above procedure are visually compared with a Landsat-TM and a MODIS mosaic images of the study site at Bistret. Apparently there is no one technique that is consistently better partly because of data noise even though the images were first subjected to speckle filtering and geometric corrections, and partly because SAR images could appear dark not only because of flooding but also because of smooth surfaces and other factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".