Constraint-Safe Graph-Context Scoring for Stable Point-Feature Labels Under Text-Width and Accessibility-Inspired Profiles
Read the original on arXiv AI →The paper introduces LABELSENSE-Pilot, a prototype for placing point-feature labels on interactive maps that balances geometric validity, display yield, local utility, and stability during camera motion. It generates eight compass candidates per feature, scores them with a multilayer perceptron using graph-context summaries, adds a previous-placement bonus, and selects a layout via mixed-integer optimization, ensuring viewport containment, per-feature uniqueness, and pairwise clearance. Experiments on 2,500 airport data show 85.62 % label display with only 2.09 % flicker and no collisions, outperforming a handcrafted integer program in flicker reduction while maintaining comparable display rates.
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