FlatGeobuf: The Underused Geospatial Format Powering Faster, Smarter Maps
- Shavini Weliwita
- Oct 27
- 2 min read
As GIS datasets grow and cloud-native workflows evolve, delivering spatial data efficiently is becoming a critical challenge. Everyone talks about GeoParquet as the “modern” format for analytics—but when it comes to interactive maps, feature-heavy apps, and edge deployments, there’s a quieter contender that deserves attention: FlatGeobuf.
Why FlatGeobuf Matters?
FlatGeobuf combines speed, simplicity, and precision, enabling workflows that traditional formats struggle to support:
Streaming Spatial Queries: GeoParquet often requires loading entire column chunks. FlatGeobuf’s packed Hilbert R-tree index at the file header lets you stream only the features in your bounding box over HTTP. No servers, no databases—just a static file.
Blazing Performance: On a 2.5M polygon dataset, FlatGeobuf reads the full dataset 8x faster than Shapefile and 2x faster than GeoPackage. Spatial filter queries operate at OpenStreetMap scale, serving results in milliseconds. Writing data is also 30% faster than Shapefile with a spatial index included.
Direct Memory Access: Built on Flatbuffers with 8-byte alignment, FlatGeobuf eliminates deserialization overhead, fetching only what your query needs.
Universal Compatibility: GDAL supports it, QGIS reads it natively, and browsers can consume it with Leaflet or Mapbox GL JS. It works wherever GeoJSON works—but orders of magnitude faster.
Lossless Precision: Unlike Mapbox Vector Tiles, FlatGeobuf delivers tile-like performance without sacrificing accuracy.
Tradeoffs
FlatGeobuf isn’t for every workflow:
No random writes—you can’t update single features without rewriting the file.
Best for static or slowly-changing datasets, not real-time collaborative editing.
Requires proper CORS/CDN setup for HTTP Range requests.
Not designed for map tile rendering.
Bottom Line
For interactive maps, feature services, or edge-deployed apps that need spatial filtering before loading data, FlatGeobuf offers an elegant, high-performance solution. It’s not flashy, it’s underfunded—but it solves a real problem efficiently.
Explore it yourself: Check out the FlatGeobuf documentation here.
This focus on efficient, cloud-native data delivery is core to what we do at Nika. On the NikaPlanet platform, we leverage technologies like FlatGeobuf to help organizations move beyond legacy GIS, unlocking the full potential of their spatial data with AI-powered analysis and collaborative workflows.
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About Nika
Nika is the company behind NikaPlanet, the planetary scale spatial data and AI platform. We're helping organizations move beyond legacy GIS systems to unlock the full potential of their spatial data with cloud-native technology, AI-powered analysis, and collaborative workflows. Our mission is to make spatial intelligence accessible and actionable for every organization.
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