Introduction

As geospatial data grows in volume and dimensionality, traditional 2D maps no longer suffice for many use cases. Urban planners need 3D building models, drone operators require photogrammetric point clouds, and logistics teams benefit from globe-scale route visualization. Three open-source tools have emerged as leaders in web-based 3D geospatial visualization — CesiumJS for digital globe applications, deck.gl for large-scale data overlays, and Kepler.gl for geospatial analytics dashboards.

This guide compares these tools across architecture, performance, and use cases, with deployment examples for self-hosted environments.

Platform Comparison

FeatureCesiumJSdeck.glKepler.gl
GitHub Stars15,37414,25611,866
RenderingWebGL globe/3DWebGL layersWebGL (via deck.gl)
Coordinate SystemWGS84/ECEF 3DWeb Mercator + 3DWeb Mercator
Point CloudsNative (3D Tiles)Via PointCloudLayerLimited
Vector DataGeoJSON, KML, CZMLGeoJSON, CSV, ArrowGeoJSON, CSV
TerrainGlobal terrain + imageryColumnLayer extrusionsGrid/Hexagon only
Time-DynamicNative CZML timelineVia custom animationNative time slider
Self-HostedYes (static files)Yes (npm/webpack)Yes (static HTML)
API ComplexityHighMediumLow (UI-driven)
Mobile SupportGoodGoodLimited

Why Self-Host 3D Geospatial?

3D geospatial data — point clouds from drone surveys, BIM models of infrastructure, real-time satellite tracks — often contains proprietary or sensitive information. Uploading this data to commercial cloud platforms introduces security risks and data residency compliance issues. Self-hosting gives you full control over who can access your 3D data.

Performance is another key advantage. Commercial 3D globe platforms stream terabytes of terrain and imagery from centralized servers. For organizations with local data sources (internal building models, drone survey outputs, sensor networks), self-hosting eliminates network latency and API rate limits. A local CesiumJS deployment with self-hosted 3D Tiles can render city-scale models at 60 FPS without any internet dependency.

For organizations already running self-hosted GIS infrastructure — described in our GIS raster processing guide and geospatial catalog platforms guide — adding a 3D visualization frontend completes the stack. For drone operators, our drone ground control stations guide covers the flight planning side of aerial data collection.

CesiumJS: The Digital Globe Standard

CesiumJS is the most comprehensive 3D geospatial engine available — it renders a full WGS84 globe with terrain, imagery, 3D buildings, and dynamic entities. Originally developed by AGI for aerospace applications, CesiumJS has become the standard for satellite tracking, drone visualization, and digital twin platforms.

Self-Hosted Setup

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<!DOCTYPE html>
<html>
<head>
  <script src="/lib/cesium/Cesium.js"></script>
  <link rel="stylesheet" href="/lib/cesium/Widgets/widgets.css" />
  <style>#cesiumContainer { height: 100vh; width: 100vw; }</style>
</head>
<body>
  <div id="cesiumContainer"></div>
  <script>
    Cesium.Ion.defaultAccessToken = '';  // Not needed for self-hosted
    
    const viewer = new Cesium.Viewer('cesiumContainer', {
      terrain: Cesium.Terrain.fromWorldTerrain(),
      baseLayerPicker: false
    });
    
    // Add a 3D building model (3D Tiles)
    const tileset = viewer.scene.primitives.add(
      new Cesium.Cesium3DTileset({
        url: '/data/buildings/tileset.json'
      })
    );
    
    // Fly to location
    viewer.camera.flyTo({
      destination: Cesium.Cartesian3.fromDegrees(116.4074, 39.9042, 5000),
      orientation: {
        heading: Cesium.Math.toRadians(0),
        pitch: Cesium.Math.toRadians(-45),
        roll: 0
      }
    });
  </script>
</body>
</html>

CesiumJS supports 3D Tiles — an OGC community standard for streaming massive 3D datasets — enabling efficient rendering of city-scale building models, point clouds, and BIM data. Its CZML format enables time-dynamic visualization, perfect for showing satellite orbits, flight paths, or vehicle trajectories over time.

Key Strengths

  • Full globe rendering: High-precision WGS84 rendering with terrain, imagery, and atmosphere
  • 3D Tiles streaming: Efficiently streams massive datasets with level-of-detail
  • Time-dynamic: Native support for time-series geospatial data with playback controls
  • Entity API: Rich API for adding 3D models, billboards, polylines, and volumes

CesiumJS is the heaviest option — the full build is several megabytes — but it is the only tool in this comparison that provides a true digital globe with global terrain and imagery support.

deck.gl: GPU-Accelerated Data Visualization Layers

deck.gl takes a fundamentally different approach — rather than rendering a globe, it provides high-performance visualization layers that overlay on any base map (MapLibre, Google Maps, or a flat WebGL view). Its strength is handling massive datasets — millions of points, arcs, polygons — that would bring traditional map libraries to a crawl.

Self-Hosted Integration

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import { Deck } from '@deck.gl/core';
import { ScatterplotLayer, ArcLayer } from '@deck.gl/layers';
import { Map } from 'react-map-gl/maplibre';

// deck.gl layer configuration
const layers = [
  new ScatterplotLayer({
    id: 'points',
    data: '/data/sensors.json',
    getPosition: d => [d.longitude, d.latitude],
    getRadius: d => Math.sqrt(d.value) * 100,
    getFillColor: [255, 140, 0],
    pickable: true
  }),
  new ArcLayer({
    id: 'flows',
    data: '/data/flows.json',
    getSourcePosition: d => [d.fromLng, d.fromLat],
    getTargetPosition: d => [d.toLng, d.toLat],
    getSourceColor: [0, 128, 255],
    getTargetColor: [255, 0, 128],
    getWidth: 2
  })
];

const deckgl = new Deck({
  canvas: 'deck-canvas',
  initialViewState: {
    longitude: 116.4074,
    latitude: 39.9042,
    zoom: 11,
    pitch: 45
  },
  controller: true,
  layers
});

deck.gl’s layer catalog is extensive: scatterplots, arcs, hexagons, heatmaps, polygons, paths, point clouds, terrain, and 3D tiles. Each layer is GPU-accelerated and can handle datasets with millions of records without performance degradation.

Key Strengths

  • Massive data handling: GPU-based rendering handles millions of data points
  • Layer composability: Mix and match visualization layers on the same map
  • Framework integration: First-class React bindings, works with any frontend framework
  • Basemap agnostic: Layers render on top of any map provider

deck.gl is a visualization framework, not a full map engine — it requires a separate basemap library (typically MapLibre GL JS or react-map-gl). This modularity is both a strength (flexibility) and a complexity (more dependencies to manage).

Kepler.gl: Geospatial Analytics for Everyone

Kepler.gl is built on top of deck.gl but takes a completely different approach — it is a complete application rather than a library. Users upload data (CSV, GeoJSON) through a drag-and-drop interface and build visualizations through a UI panel, with no coding required.

Self-Hosted Deployment

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# Clone and build
git clone https://github.com/keplergl/kepler.gl
cd kepler.gl
npm install
npm run build

# Serve the demo app
cd examples/demo-app
npm install
npm start

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<!-- Or embed directly in your page -->
<!DOCTYPE html>
<html>
<head>
  <script src="https://unpkg.com/kepler.gl@latest/umd/keplergl.min.js"></script>
  <link rel="stylesheet" href="https://unpkg.com/kepler.gl@latest/umd/keplergl.min.css" />
</head>
<body>
  <div id="app" style="height: 100vh;"></div>
  <script>
    KeplerGl.keplerGlInit({
      app: document.getElementById('app'),
      mapboxApiUrl: 'https://api.maplibre.org/maplibre-gl-js/v3/'
    });
  </script>
</body>
</html>

Kepler.gl’s key differentiator is its accessibility — a business analyst can upload a CSV of store locations and sales data and build an interactive geospatial dashboard in minutes, with filters, time sliders, layer blending, and custom color scales.

Key Strengths

  • Zero-code interface: Full dashboard creation through UI panels
  • Exportable configurations: Map configurations saved as JSON for sharing and versioning
  • Layer types: Point, arc, line, heatmap, grid, hexbin, polygon, H3 hexagons
  • Built-in analytics: Filtering, brushing, time-series animation

Kepler.gl is not designed for embedding custom applications — it is a standalone tool for data exploration. For programmatic 3D mapping, deck.gl or CesiumJS are more appropriate.

Choosing the Right 3D Platform

  • Choose CesiumJS for real digital globe applications — satellite visualization, aerospace, defense, and any project requiring global terrain, WGS84 precision, and 3D Tiles streaming
  • Choose deck.gl for data-heavy geospatial dashboards — logistics analytics, urban data visualization, movement pattern analysis, and any application with millions of data points
  • Choose Kepler.gl for exploratory geospatial analytics — when you need business users to create maps from CSV files without developer involvement

FAQ

Do I need a Cesium ion account to use CesiumJS?

No. CesiumJS is fully open-source (Apache 2.0) and can be used completely self-hosted without any Cesium ion account. The ion platform provides convenient access to global terrain, imagery, and 3D building data — but for self-hosted deployments, you can serve your own terrain tiles, imagery, and 3D Tiles datasets.

Can deck.gl render 3D buildings like CesiumJS?

deck.gl can render extruded 3D building footprints via the PolygonLayer or GeoJsonLayer with elevation, but it does not provide textured building models or global 3D building data out of the box. For realistic 3D city models, CesiumJS with 3D Tiles is the better choice. deck.gl excels at abstract data visualization (arcs, flows, heatmaps) rather than photorealistic rendering.

How do I convert drone survey data for these platforms?

For CesiumJS, convert point clouds to 3D Tiles format using tools like Cesium ion’s pipeline or the open-source obj2gltf and 3d-tiles-tools. For deck.gl, load point cloud data as GeoJSON or binary arrays and use the PointCloudLayer. Drone orthomosaics can be served as standard XYZ/TMS raster tiles and consumed by all three platforms. Our geospatial raster processing guide covers the preprocessing pipeline.

Are these tools accessible on mobile devices?

CesiumJS has good mobile support with touch-optimized controls and adaptive rendering quality. deck.gl performs well on mobile devices with WebGL 2.0 support, though complex visualizations may need layer simplification. Kepler.gl is primarily designed for desktop use — the UI panels and controls are not optimized for small screens.

What about serving my own 3D Tiles?

3D Tiles can be served as static files from any HTTP server (Nginx, Apache, S3, CloudFront). No special server software is required — the tileset.json file references individual tile files which the client fetches on demand. For large datasets, a CDN is recommended to minimize latency.

How do these compare to commercial alternatives?

CesiumJS competes with Cesium ion (the commercial cloud service), Mapbox GL JS 3D, and ArcGIS API for JavaScript. deck.gl competes with Mapbox custom layers, CARTO VL, and Google Maps visualizations. Kepler.gl competes with Tableau, CARTO Builder, and Felt. The open-source advantage is full customization and zero per-user costs — but commercial tools often provide managed data hosting that simplifies deployment.

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