Summary
Placing labels on interactive maps so they don’t overlap, flicker, or disappear awkwardly is hard, especially when labels change size due to language or accessibility settings. The authors present LABELSENSE-Pilot, a system that scores possible label positions using a neural network and picks the best arrangement while making sure labels stay readable and stable as you move the map. They tested it on thousands of airport locations worldwide and found it balanced showing many labels while reducing flicker and avoiding collisions better than previous methods. The study focuses on engineering trade-offs rather than full accessibility or user preferences.
What this means in practice
- •For interactive map developers: Integrate LABELSENSE-Pilot to improve label stability and reduce flicker when users pan or zoom maps with multilingual and accessibility-aware labels.
- •For geographic information system teams: Use constraint-safe label layouts to ensure airport or point-feature labels remain clear and non-overlapping on dense map views under various display settings.
Abstract
Point-feature label placement on interactive maps must reconcile geometric validity, display yield, local placement utility, and stability across camera motion. Accessibility and multilingual requirements further change label dimensions, yet algorithmic evaluations often collapse these concerns into overlap counts. We present LABELSENSE-Pilot, a reproducible prototype that generates eight compass candidates per feature, scores candidates with a multilayer perceptron over graph-context summaries, adds a previous-placement bonus, and selects a layout through mixed-integer optimization. The executed scorer is deliberately not described as a graph transformer. Every returned layout is checked for viewport containment, per-feature uniqueness, and pairwise clearance. Experiments use 2,500 airport coordinates and names spanning 155 countries, with country-grouped splits and generated density, camera, text-suffix, preference, and enlarged-font stressors. Across five seeds, LABELSENSE-Pilot displayed 85.62 percent of labels with 2.09 percent flicker and zero collisions. Versus a handcrafted-utility integer program, LABELSENSE-Pilot sacrificed 1.43 percentage points of display while reducing flicker by 12.04 points. Enlarged-box-aware layouts produced zero proxy violations, whereas standard geometry reevaluated at 1.5x violated 52.57 percent of selected placements. These results establish an auditable engineering trade-off, not human accessibility, multilingual usability, or preference. Official recent baselines and participant evidence remain required before submission.