HiFIVE: High-Fidelity Vector-Tile Reduction for Interactive Map Exploration

2026-03-10

AI Summary

This work addresses the challenges of slow loading and interactive latency in client-side rendering of large-scale spatial data, caused by the excessive size of vector tiles. To tackle this issue, the authors propose a visualization-aware tile compression framework that formally defines the tile simplification problem and proves its NP-hardness. Building on this foundation, they introduce a two-stage pruning strategy that intelligently reduces records, attributes, and values through an information entropy–driven triage mechanism coupled with a spatially aware sparsification algorithm. Experimental evaluation on terabyte-scale datasets demonstrates that the proposed method substantially reduces tile size while preserving high visual fidelity and enabling smooth interactivity, outperforming existing approaches.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Interactive visualization is a common tool for exploring large open-data repositories, where users quickly explore datasets across diverse domains. When it comes to large-scale spatial data, many existing tools rely on server-side rendering to produce small images that can be viewed at the client-side. However, most users prefer client-side rendering that allows quick styling of the data for better visualization experience. This paper presents HiFIVE, a data-management framework for scalable, high-fidelity client-side geospatial visualization. We formalize the visualization-aware tile reduction problem, which captures the trade-off between tile-size and visualization distortion, and prove its NP-hardness. HiFIVE introduces a two-stage solution combining triage and sparsification to selectively prune records, attributes, and values based on information-theoretic and spatial criteria. Experiments demonstrate substantial tile-size reductions while preserving visual fidelity and interactive performance at terabyte scale.

Problem

• vector-tile reduction
• client-side rendering
• visualization fidelity
• interactive map exploration
• spatial data

Innovation

• vector-tile reduction
• client-side rendering
• visualization-aware optimization
• spatial data management
• interactive map exploration