antvis/l7

🌎 Large-scale WebGL-powered Geospatial Data Visualization analysis engine.

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Summary Information

Updated 1 hour ago
Added to GitGenius on September 17th, 2026
Created on November 21st, 2018
Open Issues & Pull Requests: 216 (+0)
GitHub issues: Enabled
Number of forks: 656
Total Stargazers: 4,063 (+0)
Total Subscribers: 75 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 0.0 hours
Mean response time: 83.5 days
90th percentile: 20.0 days
Tracked items: 227

Most active contributors

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How this project is maintained

99% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Three people close 81% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 75
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 547 days
Stale 30+ days: 75
Stale 90+ days: 73

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (45)
  • question (17)
  • help wanted (16)
  • feature (7)
  • good first issue (7)
  • 🌟 star (7)
  • fixed (5)
  • 🤔 need reproduce (5)

Most active issues this week

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Detailed Description

L7 is a WebGL-powered geospatial data visualization engine designed for rendering large-scale geographic datasets on interactive maps.

The tool addresses the challenge of visualizing massive geospatial datasets efficiently by leveraging WebGL for hardware-accelerated rendering. It works by providing a layer-based architecture that allows developers to add multiple visualization layers to map instances, with support for various geometry types including points, polygons, and lines. The engine handles the complexity of transforming raw geospatial data into performant visual representations without requiring developers to write low-level graphics code.

L7 integrates with popular mapping libraries including Mapbox and Amap, making it suitable for projects that need rich geospatial visualization on top of existing map infrastructure. The tool works well for applications requiring interactive exploration of geographic data at scale, such as heat maps, choropleth visualizations, and spatial analysis dashboards. It is particularly valuable when performance matters and datasets are too large for traditional canvas or SVG rendering approaches.

The project maintains active development with regular updates to its visualization capabilities and map integration support. The codebase shows consistent refinement of the rendering pipeline and layer system. Development activity demonstrates ongoing attention to both new feature additions and maintenance of existing functionality. The project sustains engagement with its user community through documentation and example materials.