DETERMINING A SET OF MAXIMUM INSCRIBED RECTANGLES FOR LABEL PLACEMENT IN A REGION
Bibliographic record
Abstract
Driven by the industrial challenge of labeling maps for GIS applications, we investigate the problem of computing a map region P such that a rectangular axis-parallel label L of a given size can be placed in it. The map region to be labeled is in general a non-convex n-gon which may contain holes. We first derive a new practical algorithm based on the sweep-line technique that determines the com set of Maximum Inscribed Rectangles (MIRs) in P in O(nk), where k is the size of the output, for the case when the polygon sides have an axis-parallel orientation. After the set of MIRs has been found, any subsequent query on label L placement runs in only O(logn) time. We then provide an algorithm to convert the general case to the axis-parallel case. Extensive experimentation in both laboratory and industrial settings confirms that the developed method is practical and highly efficient for processing large GIS data sets.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".