The ‘Good Continuation’ Principle of Perceptual Organization applied to the Generalization of Road Networks
Bibliographic record
Abstract
Perceptual organization (or grouping) principles play a vital role in the understanding of images and maps, and their importance in map generalization has also long been recognized. This paper presents a brief review of the application of the principles in map generalization, and then examines their particular relevance to the generalization of road networks. It is shown how the ‘good continuation’ grouping principle can serve as the basis for analyzing a road network into a set of linear elements, here termed ‘strokes’. Further analysis allows the strokes to be ordered, to reflect their relative importance in the network. The deletion of the elements according to this sequence provides a simple and effective method of generalizing (attenuating) the network. This technique has been implemented, as part of the ‘GenSystem’ generalization software package developed at the Canada Centre for Remote Sensing. The implementation is outlined, and the effectiveness of the technique demonstrated.
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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 | 0.000 |
| 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".