Using Hachures to Construct a 3D Doline Model Automatically
Bibliographic record
Abstract
All types of relief features are usually represented by a unified digital terrain model on 3D topographic maps. Few studies have considered the differences in relief characteristics and terrain complexity. Hachures are often used to construct the relief symbols on 2D topographic maps, but they are less used on 3D topographic maps. This article uses hachures to construct a 3D doline model and divides them into three types: outlines, ridge lines, and break lines. We present the extraction method for doline outlines, consider the rules of visual perception under different illumination conditions, and study the mathematical models for representing the morphological characteristics of the doline with regard to the following aspects: width, arrangement density, and grey value of hachures. Finally, we introduce a process for 3D modelling of dolines on the basis of the above discussion. Experimental results indicate that the proposed model achieves a good 3D visual representation of dolines. Furthermore, the proposed model can also be used as a reference for the creation of 3D models of other negative relief features.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".