MapLibre Tile (MLT): Next-Gen Geospatial Data

• MapLibre Tile (MLT) is a vector tile format that uses a column-oriented, two-tier model for efficient storage, rapid decoding, and low-latency rendering of large-scale geospatial data.
• MLT leverages advanced techniques like vectorized execution and SIMD-accelerated decoding to achieve compression improvements up to 3× and processing speeds 3.7–4.4× faster than MVT.
• The format supports nested data types, 3D geometries, and hybrid encoding, enabling seamless GPU offloading for real-time, scalable map visualization.

The MapLibre Tile (MLT) format is a next-generation vector tile specification designed for efficient storage, rapid transmission, and low-latency rendering of large-scale geospatial datasets. Developed to address the limitations of the decade-old Mapbox Vector Tile (MVT) format, MLT is engineered for modern data requirements arising from advanced geospatial sensors and automated detection through artificial intelligence. MLT integrates a column-oriented, two-tier architecture providing both a persistent storage format and an explicit in-memory representation, facilitating high-performance map rendering and seamless GPU offloading.

1. Motivation and Architecture

The core motivation for MLT is the inadequacy of MVT in managing the increasing volume and complexity of contemporary geospatial data. Modern sources generate vast datasets, necessitating formats capable of scalable storage and efficient processing. MLT meets these needs through:

• Column-Oriented, Two-Tier Model: MLT unifies a column-oriented storage paradigm with an explicit in-memory layout, drawing on principles established in big data frameworks such as Apache Parquet and Apache Arrow.
• Cache and Vectorized Execution: By organizing data in columns, MLT enhances cache locality and enables vectorized operations, promoting parallel processing and efficient memory access.
• Support for Advanced Types: The format natively accommodates nested structures (lists, structs), 3D and m-coordinates, and linear referencing, expanding its applicability to complex geospatial scenarios.

This architectural foundation is intended to facilitate next-generation map rendering engines that delegate computationally intensive tasks, such as tessellation, directly to the GPU—addressing hardware advancement bottlenecks exemplified by the stagnation of Moore’s law.

2. Quantitative Performance Analysis

Empirical studies presented in (Tremmel et al., 14 Aug 2025) provide quantitative evidence of MLT's superiority over MVT, particularly in the domains of compression, decoding, and processing:

Metric	MLT vs. MVT	Typical Improvement	Largest Improvement
Compression Ratio	Up to 3×	~3×	Up to 6× (large tiles)
Decoding Speed	Up to 3×	2.0–3.1×	—
Filtering/Processing	3.7–4.4×	—	—

• Compression: MLT encodes tilesets at up to three times the efficiency of MVT; certain large tiles are up to six times smaller. Typical encoded tile sizes are approximately one-third or less than half of their MVT equivalents.
• Decoding: JavaScript benchmarks across diverse datasets (OpenStreetMap derivatives, national base maps, Overture Maps) indicate decoding accelerations of 2.0–3.1×, prior to application of explicit SIMD instruction sets.
• Processing: Vectorized, in-memory representation enables filtering and map-styling operations that are 3.7–4.4× faster than with MVT.

These findings collectively highlight MLT's scalability, supporting rapid data transmission and visualization even for voluminous basemap datasets.

3. Functional Innovations for Map Rendering

MLT introduces a suite of novel functionalities to serve evolving map rendering paradigms:

• Complex Type System: Direct support for nested types (lists, structs). The format differentiates vertex-scoped from feature-scoped attributes, permitting granular attribute management.
• Advanced Geometry Encoding: Beyond standard 2D, MLT efficiently encodes 3D coordinates using interleaved, contiguous VertexBuffer layouts.
• Pre-Tessellated Polygon Meshes: MLT stores pre-computed polygon meshes employing IndexBuffer and optional Triangles streams, reducing runtime tessellation demands on rendering hardware.
• Hybrid/Lightweight Encoding Schemes: A restricted, selected set of lightweight encoding methods, with recursive application for hybrid profiles, delivers high space efficiency and decoding speeds. Distinct “simple” and “advanced” profiles support a spectrum of implementation requirements.

Taken together, these features position MLT for direct compatibility with GPU-accelerated rendering and next-generation, WebGPU-enabled map stacks.

4. Technical Structure and Encoding Algorithms

MLT's technical design is characterized by its decomposition storage model and intricate encoding techniques:

• Column-Based Layout: Data is organized per logical column, decomposed into streams (presence, offset, length, data) to allow targeted compression and optimized memory footprint.
• Topology Representation: Separate streams (Geometries, Rings, Vertices) encode multipart features and complex topologies. A Type stream employing a union type facilitates mixed geometry storage per column.
• Coordinate Compression Workflow:

1. Dictionary encode vertex coordinates. 2. Dimensionality reduction via Z-order curve sorting. 3. Morton code assignment and delta encoding. 4. Application of SIMD-FastPFOR for lossless, high-throughput vector compression.

Mathematically, the generic encoding of VertexBuffer content is formalized as:

• For 2D: $\{(x_1, y_1), (x_2, y_2), \ldots, (x_n, y_n)\}$
• For 3D: $$\{(x_1, y_1, z_1), (x_2, y_2, z_2), \ldots, (x_n, y_n, z_n)\}$$

This structure underpins efficient data transfer and interoperability with GPU memory, supporting requirements such as 16-byte alignment for 3D coordinate vectors and facilitating SIMD and compute shader utilization.

5. In-Memory Representation and Processing

MLT converts the columnar storage into vectorized in-memory formats optimized for random access and processing efficiency:

• Vector Transformation: Data streams are mapped onto memory buffers (e.g., ArrayBuffer for web stacks) to enable constant-time access and maximized cache utilization.
• SIMD and GPU Compatibility: Data alignment and organization are specifically intended to support SIMD execution and advanced GPU compute pipelines.
• Task-Specific Optimization: Operations such as filtering are accelerated both by vectorization and by direct execution on compressed columnar vectors, which reduces memory traversal and increases throughput.

This suggests that widespread adoption of MLT could influence practices in real-time geospatial visualization, moving computational bottlenecks away from the CPU.

6. Implications for the Geospatial Data Ecosystem

MLT’s design choices and measured efficiencies have broad implications for geospatial data processing and mapping:

• Scalability: Significantly reduced file sizes and improved decoding enable rapid handling and rendering of massive basemaps.
• GPU Offloading: Features such as pre-tessellated meshes and contiguous geometries facilitate full GPU processing, a necessary advancement as the industry shifts toward GPU-centric and WebGPU maps.
• Ecosystem Influence: MLT’s hybrid encoding and complex type support may inform not only future tile formats but also broader fields requiring high-performance, real-time geospatial analytics.
• Sustainability: Elimination of heavyweight compression steps reduces energy consumption and enhances network transfer efficiency in high-bandwidth environments.

A plausible implication is the broader adoption of columnar geospatial formats in both industry and research, establishing new performance baselines for analytics and visualization workflows.

7. Summary of Format Evolution and Industry Positioning

MLT signifies an evolutionary advancement in vector tile formats, emphasizing columnar data organization and hybrid encoding strategies to meet contemporary geospatial demands. Delivering superior compression, decoding speed, and filtering efficiency, MLT incorporates advanced functionalities designed for GPU offloading and supports increasingly complex attribute and geometric types. These combined properties position the MapLibre Tile format as a foundational component for next-generation map rendering technologies in the geospatial sector (Tremmel et al., 14 Aug 2025).