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User Stories

人工衛星が取得するデータ量は急速に増大している。将来的には、地球観測衛星だけでも1日あたりペタバイト級のデータが生成されるとも予測されている。こうした膨大なデータをすべて地上に送信してから処理する従来の方式では、通信帯域や地上局数の制約から限界に近づきつつある。 

こうした課題に対し、NTTソフトウェアイノベーションセンタでは「宇宙データセンタ」と呼ばれるコンピューティングアーキテクチャの研究開発が進められている。この取り組みでは、人工衛星上でAI推論などのデータ処理を実行し、重要な情報のみを地上に送信することで、通信効率の向上と迅速なデータ活用を目指している。 

このアーキテクチャの設計や検証を支える可視化の試みとして、Webベースの3D地球可視化エンジンであるCesiumJSが活用されている。

宇宙でAIが動く新しいデータ処理モデル 

その中核の一つとなるのが、衛星軌道および衛星間通信リンクを統合的にシミュレーションできる基盤である。 

従来の地球観測ワークフローは、次のような流れで構成されていた。 

これに対し、NTTが検討している新しいモデルでは、 

重要な情報のみを地上へ送信 
という処理アーキテクチャが採用されている。  

NTTの技術展示イベント「NTT R&Dフォーラム2025」で公開されたデモでは、人工物検知アルゴリズムを用いて重要領域のみを抽出することで、送信データ量を約96%削減できる可能性が示された。 

ハイパースペクトルデータ（疑似RGB画像）: 衛星上で取得された観測データ（ハイパースペクトルデータ）の可視化例。（提供：NTT）

AI処理による重要領域抽出結果（データ削減後）: 同データに対してAI処理を適用し、重要または異常と判断された領域のみを抽出した例。（提供：NTT）

このアプローチは単なる通信効率の改善にとどまらない。 
限られた通信帯域の中で「どの情報を優先して地上へ送るか」を選別できるため、 

インフラの異常検知 
といった迅速な判断が求められる分野での応用が期待されている。  

宇宙データセンタのアーキテクチャでは、単一の衛星ではなく、複数の要素が連携する分散システムが想定されている。 
例えば次のような要素が同時に関係する。 

時間とともに変化するネットワーク構造  

宇宙データセンタの概念を可視化したもので、衛星上でのデータ処理の様子を示している。（提供：NTT） 

Visualization of a space data center concept, showing satellite-based processing in orbit. Courtesy NTT. 
宇宙データセンタの概念を可視化したもので、衛星上でのデータ処理の様子を示している。（提供：NTT）

NTT: space data center concept

NTTでは、宇宙データセンタを構成する衛星群の軌道や通信リンク、軌道上でのAI処理を統合的にシミュレーションできるライブラリも開発している。しかし、衛星数やシミュレーション期間が増えるにつれて、可視化処理の負荷が増大し、研究環境の制約になるケースも見られた。 

この課題に対し、NTTでは大規模な軌道シミュレーションのための可視化環境の構築・検証にCesiumJSを用いた。 

CesiumJSを用いた宇宙システムの可視化 

NTTが可視化の選択肢を比較検討する中で、CesiumJSのいくつかの特徴が有効であることが分かった。 

CZMLによる時間変化を伴うオブジェクト表現 
衛星軌道や通信リンクを自然に表現できる  

Webブラウザ上での高性能レンダリング 
大規模シミュレーションでも操作性を維持できる  

地理空間データとの統合 
地形や衛星画像と組み合わせて地球上の文脈で理解できる  

CesiumJSを用いることで、衛星の軌道、通信リンク、観測対象地点などを3D地球上で直感的に把握できる可視化環境が実現された。 

分散ネットワークアーキテクチャにおける衛星間通信リンクの可視化。（提供：NTT） 

Inter-satellite communication links visualized in a distributed network architecture. Courtesy NTT. 
分散ネットワークアーキテクチャにおける衛星間通信リンクの可視化。（提供：NTT）

NTT: inter-satellite communication links

また、Cesium ionの地理空間データを組み合わせることで、宇宙空間の挙動と地球上の観測対象を同時に把握することが可能になっている。

可視化・分析レイヤとしてのCesium 

本プロジェクトにおいて、Cesiumは可視化および分析のレイヤーとして機能する。 

オンボード処理パイプラインの構築や、前処理・AI推論フローの設計を担っているのは、NTTが開発した low-code / no-code のパイプライン設計ツールである。 

CesiumJS は、そうした処理構成や実行結果を3次元空間上で可視化・分析するためのレイヤとして機能している。衛星軌道、通信リンク、観測地点、センサー由来データやAI推論結果を地形やインフラと統合して表示することで、研究者やエンジニアはシステム全体の挙動を直感的に把握できる。 

Cesiumはパイプラインを「設計する」ためのインタフェースではない。一方で、複雑な宇宙分散システムがどのように振る舞っているかを“観測可能”にし、仮説検証や異常の発見、関係者間での認識共有を支援する。 

このようにCesiumは、NTTの設計ツール群と連携しながら、研究開発を加速する可視化・分析基盤として重要な役割を果たしている。 

研究開発を加速する可視化基盤 

可視化部分をCZMLベースの構成に整理したことで、ソフトウェア構成はシンプルになり、開発効率も向上した。 
その結果、研究チームは開発開始から約2か月でプロトタイプをNTT R&Dフォーラムに展示することができた。 

可視化の実装やレンダリング最適化にかかる負担が軽減されたことで、研究者はより多くの時間を、軌道計算や通信リンク成立判定、分散処理アーキテクチャ設計などの宇宙データセンタの実現に向けた中核機能の研究開発に集中できるようになった。 

現在のプロトタイプでは、主にCZMLを用いた衛星軌道や通信リンクの可視化が中心となっている。将来的に衛星コンステレーション規模が拡大し、より大規模なデータ可視化処理が必要になった場合には、3D Tilesなどの階層的ストリーミング技術の活用も検討されている。 

宇宙で処理し、地上で理解する。その橋渡し役として、空間可視化技術の重要性は今後ますます高まっていくだろう。 

Cesiumでは、NTTのように新しい宇宙コンピューティングアーキテクチャを探求する組織が、複雑なシステムを理解し共有するための基盤としてCesiumを活用していることを嬉しく思います。 

他の航空宇宙企業のCesium活用例についてもぜひご覧ください。 

NTT Explores a Space Data Center Architecture with Cesium for On-Orbit AI and Satellite Network Visualization

The volume of data collected by satellites is increasing rapidly. In the future, even Earth observation satellites alone are expected to generate data at the petabyte-per-day scale. Under the conventional approach of transmitting all of this data to the ground before processing, limitations in communication bandwidth and the number of available ground stations are becoming increasingly significant. 

To address this challenge, NTT Software Innovation Center is conducting research and development on a computing architecture referred to as the “Space Data Center.” In this approach, data processing such as AI inference is executed onboard satellites, and only important information is transmitted to the ground, with the goal of improving communication efficiency and enabling faster use of data. 

As part of the effort to support the design and validation of this architecture, NTT is using CesiumJS, a web-based 3D globe visualization engine. 

A new data processing model in which AI operates in space 

One of the core elements is a platform capable of integrated simulation of satellite orbits and inter-satellite communication links. 

A conventional Earth observation workflow has typically consisted of the following steps: 

A satellite observes Earth’s surface.  

Analysis and processing are performed on Earth.  

By contrast, in the new model being explored by NTT, the processing architecture is: 

Only important information is transmitted to the ground.  

In a demonstration presented at NTT’s technology showcase event, NTT R&D Forum 2025, an object-detection algorithm was used to extract only important regions, showing the possibility of reducing transmitted data volume by approximately 96%. 

Hyperspectral data (pseudo-RGB visualization): Example of observation data (hyperspectral data) acquired onboard a satellite, visualized as a pseudo-RGB image. Courtesy NTT. 

AI-processed result highlighting regions of interest (after data reduction): Example of applying AI processing to the same dataset, extracting only regions identified as significant or anomalous. Courtesy NTT. 

This approach is not limited to improving communication efficiency alone. 

Because it makes it possible to select which information should be prioritized for transmission to the ground within limited communication bandwidth, it is expected to be applicable in fields requiring rapid decision-making, such as: 

A distributed system expanding into space 

The Space Data Center architecture assumes not a single satellite but a distributed system in which multiple elements work together. 

For example, the following elements are involved simultaneously: 

network structures that evolve over time.  

Visualization of a space data center concept, showing satellite-based processing in orbit. Courtesy NTT. 

NTT has also developed a library capable of integrated simulation of the satellite orbits, communication links, and onboard AI processing involved in the Space Data Center. However, as the number of satellites and the simulation duration increased, the computational load of visualization also increased, becoming a constraint in the research environment in some cases. 

To address this issue, NTT conducted development and validation of a visualization environment using CesiumJS for large-scale orbital simulations. 

Visualizing space systems with CesiumJS 

As NTT evaluated different visualization options, several characteristics of CesiumJS proved effective: 

Object representation with temporal changes using CZML: 
Orbital paths and communication links can be represented naturally.  

High-performance rendering in a web browser: 
Operability can be maintained even in large-scale simulations.  

Integration with geospatial data: 
Space systems can be understood in the context of Earth by combining them with terrain and satellite imagery.  

By using CesiumJS, NTT created a visualization environment in which satellite orbits, communication links, and observation target locations can be intuitively understood on a 3D globe. 

Inter-satellite communication links visualized in a distributed network architecture. Courtesy NTT. 

In addition, by combining it with geospatial data from Cesium ion, it becomes possible to understand space-based behavior and Earth-based observation targets at the same time. 

In this project, Cesium plays a central role as a visualization and analysis layer. 

NTT’s low-code/no-code pipeline design tool provides the primary interface for configuring onboard processing workflows, including preprocessing steps and AI inference pipelines. 

CesiumJS serves as the visualization and analysis layer that complements this design environment. By visualizing system configurations, sensor data, and processing outputs in a unified 3D context, Cesium enables researchers and engineers to intuitively 

inspect, validate, and reason about complex onboard behaviors more effectively. 

Rather than acting as a pipeline authoring interface, Cesium focuses on making system behavior observable.   

This visualization-driven analysis shortens iteration cycles by helping researchers identify anomalies, evaluate assumptions, and communicate findings across interdisciplinary teams. In this way, Cesium provides a foundational layer that accelerates R&D while integrating seamlessly with NTT’s broader toolchain. 

A visualization foundation that accelerates R&D 

By reorganizing the visualization component into a CZML-based structure, the software configuration became simpler and development efficiency improved. 

As a result, the research team was able to demonstrate a prototype at NTT R&D Forum, in approximately two months from the start of development. 

By reducing the burden associated with visualization implementation and rendering optimization, researchers were able to spend more time focusing on the research and development of core functions required to realize the Space Data Center, such as orbital calculations, communication link validation, and distributed processing architecture design. 

In the current prototype, visualization mainly focuses on satellite orbits and communication links represented using CZML. In the future, as satellite constellation scale increases and larger-scale visualization processing becomes necessary, the use of hierarchical streaming technologies such as 3D Tiles is also being considered. 

Process in space, and understand on Earth. As a bridge between the two, the importance of geospatial visualization technology is expected to grow even further. 

At Cesium, we are pleased to see organizations like NTT—exploring new space computing architectures—use Cesium as a foundation for understanding and sharing complex systems. 

Please also explore how other aerospace companies are using Cesium. 

 Cesiumで可視化する軌道上AIと衛星ネットワーク ――  NTTが探る「宇宙データセンタ」アーキテクチャ

Lu Zhang is perhaps not the only person who sometimes finds running on a treadmill monotonous. During a recent workout he thought how much nicer it would be to run through some of the neighborhoods he used to live in. Since a quick flight to Paris or Shanghai for a run wasn’t realistic, he asked an AI chatbot a simple question: was it possible to recreate real-world neighborhoods in an immersive environment? That was the first time he’d heard of Cesium.

With EarthRunner, Lu is able to take his treadmill runs to cities all over the world. Courtesy Lu Zhang.

As a neuroscientist, Lu has studied spatial navigation—how the brain combines self-motion with external landmarks to represent and move through the world. With AI tools as a learning and prototyping partner, he brought that neuroscience background into geospatial technology and created EarthRunner.

The Parthenon in Google Photorealistic 3D Tiles. With Cesium for Unreal, runners can explore revisit familiar places or explore new areas. Courtesy Lu Zhang.

EarthRunner lets users run through a full-scale 3D world of real places, built with Cesium for Unreal streaming Google Photorealistic 3D Tiles. Locomotion is controlled by the treadmill, and an accompanying iPad interface, created with Python, handles navigation.

A chance to run in familiar cities made workouts more engaging. Lu further enhanced the experience with an AI-powered audio guide that highlights points of interest, making it easy to get to know new places. While EarthRunner remains a personal project, Lu sees possibilities far beyond his own workouts.

An AI-powered audio guide highlights points of interest. Courtesy Lu Zhang.

“AI gave me the confidence to try something completely new with Cesium for Unreal. Seeing the world appear almost exactly as I remembered it from my travels was completely amazing. And when I moved beyond experimenting, the Cesium documentation made it much easier to really understand the platform. After that, it felt like a whole new world opening up.” 

Lu may not have known about Cesium before he began this project, but it enabled him to take an idea sparked during a workout into an application that surpassed his original goals. Lu is now a Cesium Certified Developer, a testament to how accessible 3D geospatial development can be with the right resources. If you’re new to Cesium for Unreal, check out our tutorials to get started building your own custom application with global 3D datasets.

Bringing Earth to the Treadmill with Cesium

For industries managing vast linear infrastructure like railways and power lines, safety and reliability are paramount—and ensuring that safety requires constant monitoring and analysis of immense volumes of data. The Cross Product, a software development company specializing in LiDAR data processing, addressed this challenge by creating TCP Studio, a 3D visualization platform built with Cesium.

TCP Studio V9

From raw data to a custom visualization platform

The Cross Product recognized a critical gap in their users’ workflows: while TCP could process massive point clouds to extract meaningful information, users often lacked the specialized tools to visualize and interact with these rich datasets. This was a barrier to turning processed data into timely, actionable insights for infrastructure management.

To bridge this gap, The Cross Product developed TCP Studio, a SaaS solution built from the ground up using CesiumJS. The Cross Product also converts LAS point clouds into 3D Tiles via Cesium ion’s tiling pipeline. “Previously, we assumed clients had their own visualization tools, which was only partially true,” explained Elise Vi Nhu Ba, R&D engineer at The Cross Product. “A dedicated interface is now a significant advantage, allowing us to showcase our solutions’ capabilities through a custom-built environment with full control over the UX.”

This control is crucial. TCP Studio provides an intuitive web-based interface where clients can explore their infrastructure in a high-fidelity 3D environment, interact with processed data, identify issues, and make informed decisions—all without needing to install or maintain their own complex software.

As part of the Bentley Activate program, a startup accelerator designed to support companies building innovative solutions on digital twin platforms, The Cross Product was able to maximize the work of a small team and accelerate their work with the Cesium platform.

The TCP Studio Project Explorer provides a high-level overview of infrastructure assets, overlaid on Sentinel-2 imagery via Cesium ion. Courtesy The Cross Product. 

Speed and automation: accelerating time to value

One of the most significant advantages The Cross Product gained from building with Cesium was development speed. Following an agile approach, the team delivered the first demonstration version of TCP Studio in just three months. This rapid prototyping validated their approach and enabled them to present a working solution to their users early in the development cycle.

This speed translates directly to client value. By using Cesium ion to tile massive LAS point clouds into 3D Tiles streamed to CesiumJS, TCP Studio delivers reliable performance, even with complex scenes. The platform also features solutions for automated measurements and issue detection. For example, users can automatically detect and flag areas where the track gauge on a railway is outside of the specified tolerance. This automation saves significant time and allows engineers to focus on critical maintenance tasks.

The Cross Product points to Cesium for a 10% growth in revenue in Q1 2026 by enabling new deals and creating a recurrent revenue stream through subscriptions.

TCP Studio features automatic detection of potential issues in large infrastructure projects. Courtesy The Cross Product.

A scalable foundation for high-performance geospatial visualization and analysis

The ability to quickly build and deliver a high-performance, scalable 3D visualization and analysis solution for massive geospatial datasets was crucial for The Cross Product.

Leveraging CesiumJS for visualization and Cesium ion for data tiling and hosting, The Cross Product was able to focus on its core expertise: developing advanced LiDAR processing algorithms and creating a tailored user experience.

“Cesium enabled us to transform massive LiDAR datasets into actionable insights and empower clients to remotely manage and explore their infrastructure within a high-performance, large-scale 3D platform.”

Elise Vi Nhu Ba, R&D Engineer, The Cross Product

Users can dive into detailed 3D views of their infrastructure, create and manage issues, and analyze measurements within TCP Studio. Courtesy The Cross Product.

For large infrastructure projects, CesiumJS provides a foundation for scalable and customizable solutions for end users. For more information about this project, contact Andrés Serna, CEO, The Cross Product. Explore how open source CesiumJS can help bring 3D visualizations in browser to your users. 

The Cross Product Transforms Raw LiDAR Data into Actionable Infrastructure Insights with Cesium

Manolis Nikolakakis is a photographer who’s extensively documented his beautiful home island of Crete. When he set out to create a better way of sharing his photos, he ended up developing an entire site dedicated to Crete’s cultural and environmental heritage. Geotour.gr integrates numerous external systems to amass historical, architectural, and environmental information about the island, all organized around a CesiumJS model of Crete.

Geotour aggregates data from numerous sources to foster a better understanding of Crete, all spatially organized around a CesiumJS model of the island. Courtesy Geotour.  

Geotour: map integrations

Manolis built Geotour with help from AI, which allowed him to integrate numerous systems and dynamically fetch data for an up-to-date understanding of Crete. The site’s 3D map of Crete, created in CesiumJS, allows visitors to access all sorts of insights about the island. 

Geotour uses WordPress as a headless CMS, from which it dynamically fetches points of interest, such historical sites. Cesium parses the JSON to instantiate entities on the map, giving visitors information such as a town’s history, attractions, and population. Manolis is able to update content in the CesiumJS view by editing the associated WordPress post. The interactive CesiumJS viewer similarly allows visitors to explore live data about local flora and fauna via an integration with iNaturalist, archival materials about historical sites via an integration with SearchCulture.gr, and photos taken across the island via an integration with the Piwigo photo gallery.

“CesiumJS fits naturally into an open, API‑driven workflow. I can bring in data from WordPress, PostGIS, and open source tools and immediately see everything come together spatially. CesiumJS makes it easy to experiment and iterate quickly, which is essential when you’re building alone and exploring new ideas.” 

A CesiumJS viewer provides the spatial index for visitors to explore Crete. Courtesy Geotour.

Manolis has taken gorgeous photos all over Crete and has implemented an intuitive way of viewing spherical panoramas in CesiumJS. (At the time, bringing panoramic photos into CesiumJS was not trivial. Last month, CesiumJS added support for panoramic images.)

When visitors to Geotour navigate to a location on the island where a panoramic photo has been geotagged, an icon indicates there is a panoramic image. Clicking on the icon pulls up a Pano2VR player. The Pano2VR player is treated as a camera overlay tied to the Cesium camera, and it tracks the user's location in the background. When a user exits a photo, the Cesium camera instantly snaps to match the user's exact 2D viewing angle, keeping the transition thoroughly in sync.

Visitors to Geotour can transition smoothly between panoramic photos taken around Crete and a 3D model of the island. Courtesy Geotour.

Geotour: explorer mode

Geotour offers a similarly tight connection between the CesiumJS view and video tours created using drone or vehicle-mounted cameras. In the videos, timecodes are mapped to geospatial coordinates. By integrating CesiumJS and the YouTube IFrame player API, Manolis synchronizes the linear video with 3D movement on the CesiumJS map.

Each video plays in a pop-up window on the virtual map.  As the video plays, the Cesium camera moves along the road or drone path in the 3D environment, keeping the user's Cesium perspective synchronized with the video footage. Clicking ahead in the video or to a new location on the map will automatically sync visitors to the correct position in the other window, allowing them to explore the video by geolocation or to see the geolocation based on the video.

In Geotour, video timestamps are synced to geographic coordinates so viewers can spatially explore video tours of the area. Courtesy Geotour.

Geotour: video tours of Crete

Geotour has much more to offer, and Manolis has documented his process extensively, so whether you are interested in exploring Crete or in enhancing your use of CesiumJS, Geotour is well worth a visit. Check out our CesiumJS tutorials for guidance on working with the camera, displaying panoramic imagery, or creating entities.

Geotour Showcases Crete with Synchronized Panoramas and Video in CesiumJS

Purdue University’s Envision Center supports faculty across the university by creating 3D visualizations for research and classroom use. When researchers requested an immersive way of viewing complex spatial data, such as supernovas, heart models, and combustion engines, the Envision Center built CollabXR, a collaborative XR platform designed to ingest nearly any 3D dataset and explore it as a group. Now with the addition of Cesium for Unity, CollabXR supports geospatial data as well.

CollabXR, built by the Envision Center at Purdue University, allows people to explore complex 3D data in VR together as a group. Now with the addition of Cesium for Unity, they can also visualize geospatial datasets. Courtesy Purdue University. 

CollabXR Geospatial Visualization - Purdue Envision Center

Originally developed as a shared model viewer, CollabXR has grown into a multipurpose XR environment that supports visualizations ranging from astronomy and geoscience to cultural heritage and aviation safety. The platform runs on Meta Quest, Apple Vision Pro, and desktop systems, supporting both fully immersive VR and mixed reality.

Uluru in Australia, visualized with Cesium for Unity in CollabXR. The Purdue University Envision Center team has built an app that allows students to explore Earth using Google Photorealistic 3D Tiles. Courtesy Purdue University.

The CollabXR platform allows multiple users to engage with a single VR experience. Multi-user synchronization is handled through a networked architecture, while Unity performs client-side rendering and interaction, enabling participants to annotate, draw, and manipulate data together in real time.

“CollabXR has allowed us to bring in nearly any type of 3D model, but we’ve always wanted to be able to view geoscience data. With Cesium for Unity we’ve expanded the capabilities of CollabXR for earth and planetary sciences, cultural heritage, history, aviation, and other areas. Combining 3D Tiles with live feeds such as aviation ADS-B data, we are able to create holographic geospatial digital twins.” 

– George Takahashi, Principal Visualization Scientist, Envision Center, Purdue University

The Envision Center has brought a live ADS-B feed into CollabXR, allowing viewers to see live flights, like this visualization at the Chicago O’Hare Airport. Courtesy Purdue University.

Users of CollabXR can manipulate data in the environment. Here, a student pilot is landing a plane showing live tracked data from the university’s local Lafayette Regional Airport. Courtesy Purdue University.

On Purdue Aviation day, over 200 visitors engaged with live aviation data through CollabXR. Courtesy Purdue University.

Since integrating Cesium for Unity, the Envision Center has been able to add geospatial visualization to their repertoire. Cesium provides a high-accuracy WGS84 globe and streams real-world terrain and photogrammetry at runtime using open standards like 3D Tiles. This allows researchers to anchor domain-specific data, such as satellite orbits, atmospheric models, or live aviation feeds, directly to Earth and explore everything from city-scale sites to planet-scale environments within a single XR experience.

Google Photorealistic 3D Tiles of the Eiffel Tower in Paris, France, visualized with Cesium for Unity in CollabXR. The team built a demo visualization showing points of interest around the world. Viewers can interact with the geospatial data, highlighting key areas. Courtesy Purdue University.

Cesium’s streaming architecture makes it possible to visualize massive geospatial datasets on lightweight head-mounted devices without custom pipelines for each discipline. For students and developers, Cesium’s integration with Unity Game Objects and workflows has proven highly approachable: the majority of the artwork and code for each visualization produced by the Envision Center is done by undergraduate students, guided by a small core engineering staff.

Google Photorealistic 3D Tiles of London, England, visualized with Cesium for Unity in CollabXR. Faculty leading students on international experiences have used visualizations like this to preview trips for students before they go. Courtesy Purdue University.

The Envision Center is part of the Rosen Center for Advanced Computing, which operates the centrally maintained research computing resources at Purdue University. They’re dedicated to assisting, supporting, and collaborating with faculty, students, and industry in scientific visualization, virtual and augmented reality, and media creation. The center’s staff and student employees work with faculty partners and external clients to create VR and data visualization tools for research and educational use. The center also collaborates on grant proposals and develops promotional media, such as publication-quality stills and animated videos.

Google Photorealistic 3D Tiles of Mt Fuji, Japan, visualized with Cesium for Unity in CollabXR. Student observation of 3D geosciences, topography, and tectonic data enhances spatial exploration and communication. Courtesy Purdue University.

CollabXR is being prepared for open source release, with the goals of lowering the barrier for other universities and research groups to create shared XR environments and of expanding the reach of CollabXR use cases. By combining Unity’s XR ecosystem with Cesium’s high-accuracy geospatial platform, the Envision Center at Purdue University has created a flexible foundation for spatial computing to meet advanced research needs. To create your own real-world visualizations in XR, try out Cesium for Unity.

Purdue University Exploring the Boundaries of XR with Cesium

In complex, high-pressure environments, how information is presented can be as important as the information itself. XReality Factory (XRF) uses CesiumJS and Cesium for Unity for a single shared operational view for better, faster decisions across real operations and training scenarios, throughout defense, security, and emergency management.

XRF's tools consolidate data sources and connect agencies via a single shared operational view with 3D geospatial context for better decisions. Courtesy XRF.

XReality Factory

The volcanic eruptions on the Spanish island of La Palma in 2021 were the tipping point for XRF’s founding team, with expertise in extended reality and interactive technologies, to build tools to consolidate data sources and connect agencies via a single shared operational view with 3D geospatial context for better decisions.

“What we were developing in gaming is useful in emergencies, useful to our heroes,” said Raul Espada, XRF’s CRO.

XRF supports users in critical infrastructure, defense, emergency management, and security, working with the Spanish Armed Forces, Royal Netherlands Armed Forces, public authorities, and industry. In NATO exercises with armed forces from multiple nations on different systems, XRF tools served as the common operating picture (COP), enabling coordination across distributed teams, improving situational awareness, and accelerating decision-making with meaningful information, instead of a barrage of siloed data. The company is part of NATO’s EU Defence Innovation Scheme (EUDIS) and Defence Innovation Accelerator for the North Atlantic (DIANA) programs.

In NATO exercises, XRF tools served as the COP, enabling coordination across distributed teams. Courtesy XRF.

At the core of XRF’s dual-use command and control (C2) system is Compass, a platform for fusing data from several sources into a trusted 3D COP. With Cesium World Terrain or Cesium World Bathymetry via Cesium ion as Compass’s base, users add their own 3D models, drone imagery, and other information layers specific to their missions and organizational requirements for rapid, efficient delivery, streaming as 3D Tiles into CesiumJS for the web and Cesium for Unity for XR.

Around Compass, XRF developed complementary tools:

Apolo, a mobile app for units deployed on the ground, built with CesiumJS;

Colossus, a large multitouch command table for fixed control rooms, using CesiumJS;

Sandbox, an XR multiuser command center for planning, training, and operations, built on Cesium for Unity and device agnostic across Microsoft HoloLens, Meta Quest, and Apple Vision Pro; and

Hermes, XRF’s AI system that interprets scenarios to support decisions with natural language.

With real-world data available on a variety of devices, users can more quickly understand terrain, elevation, line of sight, and spatial relationships in context.

“Our goal has always been to integrate the solution the final user needs in their unifying system, so most of our effort goes into interoperability,” said Antonio Sanchez, XRF’s primary developer.

3D Tiles, an OGC community standard built on glTF and other 3D data types, enables XRF’s users to efficiently stream massive datasets, at any scale, together. The view their users need is the one that will load with the most detail, supporting immersive visualization and analysis on both high-end workstations and bandwidth-constrained devices.

XRF uses Cesium ion SaaS for testing, demos, and situations with reliable internet. The team and its users employ Cesium ion Self-Hosted for strict internal security requirements and off-grid environments.

“The level of performance—rendering continental-scale terrain down to tactical detail, with multiple users connected—is essential for user trust. In defense and emergency contexts, if the visualization stutters or lags, the system is not usable. The feedback we’ve received confirms that the 3D view is responsive and dependable,” Sanchez said.

XRF’s users report clearer briefings, faster alignment across teams, more confident decisions, and smoother transitions because of the connected, integrated approach, with the 3D geospatial foundation as a key enabler.

The volcanic eruptions on the Spanish island of La Palma in 2021 were the tipping point for XRF’s founding team. Pictured here is La Palma in XRF's Apolo, a mobile app for units deployed on the ground, built with CesiumJS. Courtesy XRF.

The volcanic eruptions on the Spanish island of La Palma in 2021 were the tipping point for XRF’s founding team. Pictured here is La Palma in XRF's Colossus, a large multitouch command table for fixed control rooms, using CesiumJS. Courtesy XRF.

XRF estimates that using Cesium reduced its time to first operational deployments by 30-40%. Because the team does not need to build or maintain the core 3D terrain or streaming architecture, they are able to focus on additional features—and lower their ongoing costs by replacing disconnected tools with one shared system, just as they’re doing for their users in defense, security, and emergency management.

Learn more about optimizing, hosting, and streaming your data in an immersive, shared source of truth for time-sensitive action.

Hear from XRF at the 2026 Cesium Developer Conference. Register to join us in Philadelphia in June.

XRF Connects and Integrates Missions with Cesium

As the Orion spacecraft journeyed around the moon during the historic Artemis II mission, space enthusiasts around the world have tracked its progress with Orbit Codex’s online flight tracker. Built on CesiumJS, the flight tracker offers an interactive 3D view of the spacecraft’s location and trajectory.

The Artemis II tracker in Orbit Codex shows the flight of the Orion spacecraft in CesiumJS using trajectory data from the JPL Horizons API.

Artemis II Tracker from Orbit Codex

Orbit Codex has tracked the real-time location of the Orion spacecraft from the moment of its launch using trajectory data from the JPL Horizons API. The interactive 3D capabilities in CesiumJS allow users to move forward and backward in time along the ship’s planned path, making it easy to understand orbital motion and mission phases.

Viewing the Artemis II mission in interactive 3D makes it possible to visualize the relative motion of Earth and the Moon together with Orion’s flight path.

With roots in the aerospace industry, Cesium was designed from its inception for the high precision, time dynamic demands of space visualizations. A highly accurate WGS84 ellipsoid model of Earth and skybox with correct star and Sun positions, combined with its ability to efficiently render solar system–scale data directly in the browser makes CesiumJS the engine of choice for numerous space applications.

“Orbit Codex is a massive database of over 14,000 entities across the entire solar system, including rocket launches, over 8,000 satellites, and extensive space debris. I needed a WebGL engine capable of rendering a massive swarm of live objects smoothly. Cesium is the absolute gold standard for handling that level of scale.” 

The Artemis II tracker is only one small piece of Orbit Codex. As space exploration has expanded across governments and commercial sectors, information about launches, spacecraft, and missions has become wider spread. Frustrated by the fragmented nature of space data, founder Bret van Putten harnessed his professional expertise as an AI consultant and startup founder to build a comprehensive real-time space operations platform. 

Orbit Codex brings together data from dozens of sources into one unified, interactive platform that tracks launches, spacecraft, astronauts, and missions across the solar system. Data drawn from The Space Devs API is enhanced with hand curated data and a custom Google Vertex AI pipeline that uses search grounding to autonomously collect and structure historical data from verified sources.

In addition to tracking the Artemis II mission, Orbit Codex uses CesiumJS to visualize numerous other activities in space. Here moon rovers are shown on Cesium Moon data. Courtesy Orbit Codex.

Across Orbit Codex, CesiumJS acts as both a visualization engine to illustrate concepts and as a spatial index for accessing the site’s datasets. Users can dive into a detailed dossier on any entity and then instantly launch into a real-time 3D environment showing a base’s launch pads, a satellite’s current orbit, or a planet’s surface.

Visitors to Orbit Codex transition seamlessly between detailed information about numerous space topics and 3D visualizations in CesiumJS, like this view of the launch pads at Cape Canaveral, Florida.

Orbit Codex’s extensive encyclopedia covers 18 categories, from moonbases to rovers. Visitors can explore detailed views of more than 350 launch and landing sites in CesiumJS, read about propulsion systems, view a catalog of the more than 350 organizations working in space, and read the profiles of more than 800 astronauts. They can also track over 20,000 Earth-orbiting satellites and debris in a CesiumJS visualization that pulls TLE data from the CelesTrak and Space-Track catalogs. Space debris entries are enhanced by ESA DISCOS data describing physical properties such as mass and dimensions. Visitors will also find models of all eight planets in the solar system; Orbit Codex uses Cesium Mars and Cesium Moon for off-Earth terrain and imagery. 

Orbit Codex is a comprehensive repository of information about space, bringing together historical and current data from public and private enterprises, as with this entry on the Athena lunar lander.

As this Artemis mission draws to a close, Orbit Codex’s journey is just beginning. The site is growing to become a “space time machine” that allows users to scrub back to 1969 to watch a replay of Apollo 11, to follow active missions from launch to touchdown, or to look ahead to future space infrastructure, such as moon bases, habitats, and space elevators.

Cesiumで可視化する軌道上AIと衛星ネットワーク ―― NTTが探る「宇宙データセンタ」アーキテクチャ

Bringing Earth to the Treadmill with Cesium

The Cross Product Transforms Raw LiDAR Data into Actionable Infrastructure Insights with Cesium

Geotour Showcases Crete with Synchronized Panoramas and Video in CesiumJS

Purdue University Exploring the Boundaries of XR with Cesium

XRF Connects and Integrates Missions with Cesium

Orbit Codex Tracks Flight of Artemis II in CesiumJS