An Exploration of Voxel/Connection and Image Segmentation Approaches to Derive High Resolution Rasters of Manning’s n
Bibliographic record
Abstract
Manning’s roughness coefficients (Manning’s n) are numerical values which represent the resistance to flows in river channels and floodplains. Field surveys and land use land cover (LULC) maps are commonly used to infer these Manning’s n values. Field surveys can be time and labour intensive while the LULC maps derived from Landsat and RadarSat-2 are often too coarse and may produce inaccurate representations of true surface roughness, leading to errors in the outputs from hydraulic models. In this work we tested two approaches to generate high resolution surfaces depicting Manning’s n. The first is using unclassified LiDAR point clouds and a voxel/connection (VC) based approach in combination with a rule-based table to set the roughness values. In the second method, we created training data for several land use categories and trained an Image Segmentation (IS) U-Net model to predict the category of each pixel and then applied a Manning's n value to each category. Our results indicate the LiDAR based approach is able to label all pixels within the study region, but the reclass table needs more attention. The IS method has the capability to cover a larger area more efficiently, but many pixels did not fit the existing training labels, leaving the output data incomplete. Future work is needed before either of these techniques can be reliably implemented.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".