A Natural-Rule-Based-Connection (NRBC) Method for River Network Extraction from High-Resolution Imagery
Bibliographic record
Abstract
This study proposed a natural-rule-based-connection (NRBC) method to connect river segments after water body detection from remotely sensed imagery. A complete river network is important for many hydrological applications. While water body detection methods using remote sensing are well-developed, less attention has been paid to connect discontinuous river segments and form a complete river network. This study designed an automated NRBC method to extract a complete river network by connecting river segments at polygon level. With the assistance of an image pyramid, neighbouring river segments are connected based on four criteria: gap width (Tg), river direction consistency (Tθ), river width consistency (Tw), and minimum river segment length (Tl). The sensitivity of these four criteria were tested, analyzed, and proper criteria values were suggested using image scenes from two diverse river cases. The comparison of NRBC and the alternative morphological method demonstrated NRBC’s advantage of natural rule based selective connection. We refined a river centerline extraction method and show how it outperformed three other existing centerline extraction methods on the test sites. The extracted river polygons and centerlines have a multitude of end uses including rapidly mapping flood extents, monitoring surface water supply, and the provision of validation data for simulation models required for water quantity, quality and aquatic biota assessments. The code for the NRBC is available on GitHub.
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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 | 0.001 | 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 | 0.000 |
| 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 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".