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maplibre-tile-spec: Next-gen vector tile format

MapLibre Tile (MLT) defines a modern vector tile format designed for high-performance geospatial data processing. It improves upon existing formats with enhanced compression, decoding, and support for next-generation geospatial data.
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MapLibre Tile (MLT) is a next-generation vector tile specification created to address limitations in existing formats like Mapbox Vector Tile (MVT). MLT aims to handle rapidly growing geospatial data volumes and support complex geospatial sources, leveraging modern hardware and APIs. It delivers high-performance processing for large-scale 2D and 2.5 basemaps, particularly for modern graphics APIs.

MLT offers significant improvements in compression ratios, achieving up to 6x reduction on large encoded tiles. Its fast lightweight encodings enhance decoding performance, enabling efficient use of SIMD instructions. Furthermore, it natively supports linear referencing and m-values for upcoming data sources such as Overture Maps, and handles 3D coordinates and complex data types.

  • Compression: Achieves up to 6x compression ratio for large tiles.
  • Decoding: Utilizes fast lightweight encodings for improved performance.
  • Linear Referencing: Supports linear referencing and m-values for next-gen data.
  • 3D Coordinates: Provides native support for 3D coordinate systems.
  • Complex Data: Handles complex data types like nested properties and lists.
  • GPU Optimization: Formats are designed for efficient processing on both CPU and GPU.
  • JavaScript Decoder: Includes a JavaScript decoder for client-side processing.

The MapLibre Tile specification is considered stable as of October 2025, building on extensive research and development. Ongoing implementation efforts are actively underway, with a growing community contributing to its adoption. Comprehensive documentation and test suites are available, indicating a solid foundation and active maintenance.

MapLibre Tile benefits developers and organizations dealing with large geospatial datasets by offering superior performance, efficiency, and support for modern geospatial data formats. It facilitates faster data loading, reduced storage costs, and improved rendering speeds for applications like mapping, GIS, and location-based services, offering a valuable alternative to existing vector tile formats.

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Updated 6 days ago

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