Length-Preserving Thinning Algorithm for Line Extraction from Landcover Data
Bibliographic record
Abstract
This article presents a methodology to automate linear feature generalization from image data using a length-preserving thinning algorithm. Conventional thinning algorithms on raster data produce erroneous skeletons at the river edges in the estuary of a river and in the perimeter of the input data. A river skeleton should be connected to the sea through the centre line of an estuary. A related problem is that of shortened skeletons resulting from the boundary-peeling process. This article proposes a length-preserving thinning (LEPT) algorithm that consists of edge-finding, boundary-peeling, skeleton-growth, and noise-removal procedures. The edge-finding procedure finds the edges of a river and the sea and extends the perimeter of the input data by one pixel, thus removing skeleton errors at the edges in the estuary and the perimeter. The boundary-peeling procedure produces a shortened skeleton. The skeleton-growth procedure extends a shortened skeleton to the original river edge based on the direction of the skeleton. The noise-removal procedure removes isolated water spots. This novel thinning algorithm has been applied here to extract a skeleton of the Pascagoula River in Mississippi.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".