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Present at the 2025 Cesium Developer Conference

Cesium for Omniverse v0.24.0

Cesium for Unity Samples v1.15.1

CesiumJS 1.126

Cesium ion

In January, we shared our code review guide, which we recently created and made available in our new public community repo, celebrated Danny’s Patriot Award from Pennsylvania Employer Support of the Guard and Reserve, and began calling for presentations for the 2025 Cesium Developer Conference. We look forward to seeing you in Philadelphia in June!

We made improvements across Cesium this month. To get notifications about our monthly releases, you can subscribe to the Cesium release roundup thread on our community forum.

CesiumJS 1.126 is now available. Highlights of the release include:

Add ITwinData.loadGeospatialFeatures(iTwinId, collectionId) function to load data from the Geospatial Features API. #12449

Fixed error when resetting Cesium3DTileset.modelMatrix to its initial value. #12409

Fixed the parameter types of the ClippingPolygon.equals function and fixed cases where parameters to equals functions had erroneously not been marked as 'optional'. #12394

Fixed Draco decoding for vertex colors that are normalized UNSIGNED_BYTE or UNSIGNED_SHORT. #12417

Cesium for Unreal v2.13.1 is now available. Highlights of this release include:

Added CesiumUrlTemplateRasterOverlay, allowing a raster overlay to be added using tiles requested based on a specified URL template.

Added EstimateTimeZoneForLongitude method to ACesiumSunSky to set a reasonable TimeZone value at the given longitude.

The "Place Georeference Origin Here" button on ACesiumGeoreference will now adjust the time zone of the ACesiumSunSky instances that reference it, based on the new origin's longitude. This improves user experience when moving the origin to locations where it would be nighttime in the current time zone. A new EstimateTimeZoneForLongitude method on ACesiumSunSky can also be invoked manually from C++ or Blueprint.

Added RequestHeaders property to Cesium3DTileset, and the various Raster Overlay classes, allowing custom headers to be specified.

Worked around a limitation in Unreal's FMatrix -> FTransform conversion that prevented models with a small scale factor (e.g., where vertex positions are expressed in millimeters) from rendering because their scale was treated as 0.0.

Check the Cesium for Unreal release and the Cesium for Unreal Samples release for the full list of updates.

Download the Cesium for Unreal plugin from Epic Marketplace

Cesium for Unity v1.15.1 is now available. Highlights of this release include:

Exceptions in either C# or C++ code are now handled much more reliably.

Check the Cesium for Unity changelog for the full list of updates, and follow our Cesium for Unity tutorials to get started. 

Snowdon Sample Model from Autodesk Revit, streamed with 3D Tiles and visualized using Cesium for Unity. Featured in Level 08_CesiumArchitecturalDesign in Cesium for Unity Samples.

Cesium OSM Buildings and Cesium OSM Buildings for Cesium World Bathymetry datasets have been updated based on the OSM Timestamp of Jan 13 00:00 GMT 2025.

In addition to the Bing geocoder, Cesium ion now provides the Google geocoder for use in your applications. See this sandcastle for implementation.

In addition to the Bing geocoder, the Google geocoder is now available to applications using Cesium ion Self-Hosted when your Self-Hosted installation is connected to your Cesium ion SaaS account. See this sandcastle for implementation.

Cesium ion Self-Hosted v1.6.0, including the ability to upload data from Azure for tiling, will be released no later than early March 2025.

Check the changelogs for the full list of updates for Cesium ion SaaS and Cesium ion Self-Hosted.

DroneDeploy enables stakeholders to examine the entire life cycle of a project with its drone software and viewer, which use 3D Tiles and Cesium ion Self-Hosted to make massive photogrammetry datasets manageable and accessible. With Cesium’s 3D Tiling Pipeline, DroneDeploy puts its engineering resources into its custom viewer rather than model processing, and users get photorealistic 3D models for decisions based on reality.

Capture high-resolution aerial data to align teams and keep AEC projects on track. Courtesy DroneDeploy.

DroneDeploy Map Engine

In December, we announced the release of the Cesium Design Tiler and Cesium ion for Autodesk Revit Add-In and dug into Reality Tiler v2. Did you keep an eye on Saint Nick with NORAD Tracks Santa on Christmas Eve?

The Cesium Design Tiler and Cesium ion for Autodesk Revit Add-In are now available.

Cesium Design Tiler

CesiumJS 1.125 is now available. Highlights of the release include:

Expanded the integration with the iTwin Platform to load GeoJSON and KML data from the Reality Management API. Use ITwinData.createDataSourceForRealityDataId(iTwinId, realityDataId) to load data as the corresponding Data Source. #12344

Added environmentMapOptions to ModelGraphics. For performance reasons, the environment map will not update if the entity position changes by default. If environment map updates based on entity position are desired, ensure to provide an appropriate environmentMapOptions.maximumPositionEpsilon value. #12358

Reduced memory usage and performance bottlenecks when using environment maps with models. #12356

Fixed JulianDate to always generate valid ISO strings for fractional milliseconds. #12345

Fixed intermittent z-fighting issue. #12337

iTwin Reality Feature Service results displayed as GeoJSON with iTwin Reality Point Cloud data in CesiumJS.

Cesium for Unreal v2.12.0 is now available. Highlights of this release include:

Removed support for Unreal Engine 5.2. Unreal Engine 5.3 or later is now required.

Renamed FCesiumFeatureIdAttribute::GetFeatureIDForVertex to FCesiumFeatureIdAttribute::GetFeatureID.

Renamed FCesiumFeatureIdAttribute::GetVertexCount to FCesiumFeatureIdAttribute::GetCount.

Added support for metadata querying and styling on instanced models.

Fixed a bug in CesiumSubLevelSwitcherComponent that could prevent all sub-levels from loading if a single sub-level failed to load.

This release updates cesium-native from v0.42.0 to v0.43.0. See the changelog for a complete list of changes in cesium-native.

Cesium for Unity v1.14.1 is now available. This is the last release of Cesium for Unity that will support Unity 2021 LTS (2021.3). Future versions will require Unity 2022 LTS (2022.3) or Unity 6.

Fixed a bug that could cause a crash on AppDomain reloads.

Fixed a bug that could cause a crash or incorrect textures when multiple Cesium3DTileset tiles referenced the same image by URL.

Fixed a bug that could cause incorrect colors in a model that did have vertex colors but did not have normals.

Fixed a bug that could cause a hang when attempting to load a model with UINT16 indices where generating flat normals required more than 2^16 vertices.

Fixed a bug in the Abseil vcpkg overlay port that could cause linker errors on some systems.

The new Cesium Design Tiler tiles IFC and glTF models into 3D Tiles. Read more.

The Cesium ion for Autodesk Revit Add-In is available on the Downloads page. This Add-In uploads your current 3D View directly to Cesium ion and tiles it with the Design Tiler to produce 3D Tiles.

Cesium Stories now include annotation features for points, polygons, and polylines. Check out an example here.

The new Design Tiler is now available in the Self-Hosted UI as well as the Self-Hosted CLI.

Cesium ion Self-Hosted has been updated to include more secure container-based images. This is part of a continuing effort to increase the security of the Self-Hosted version of Cesium ion.

Exposed the double-sided option for 3D Models and 3D Capture on the CLI.

Exposed a new option useCrsOrdering on the CityGML Tiler.

Fixed a regression where input models with missing textures were handled incorrectly when KTX output was enabled.

Fixed an issue that could cause a crash when generating tiles with multiple texture types.

Tool added: QuantizedMeshTo3DTilesConverter.

Support for lunar data for imagery tiler, terrain tiler, and QuantizedMeshTo3DTilesConverter.

Buildings tiler now supports using traditional GIS ordering or the ordering from the CRS.

View your Revit models anywhere in the world with accurate geospatial context.

FOX Sports uses Cesium for Unreal and Google Photorealistic 3D Tiles to orient and immerse fans during FOX NFL SUNDAY.

Cesium has partnered with NORAD since 2012 to provide updates on Santa Claus’s real-time location in 3D on Christmas Eve.

In November 2023, we announced the Reality Tiler that replaced our then-current photogrammetry tiler as part of the 3D Tiling Pipelines included in Cesium ion and Self-Hosted products. We built the Reality Tiler with emphasis on scalability, tiling performance, and runtime efficiency. Today, we’re digging into Reality Tiler v2, released in July 2024. We’ll cover the complete refactor of the Reality Tiler, where we rebuilt the tiling pipeline from the ground up, bringing significant improvements to tiling time, tileset quality, and runtime performance. We’ll take a close look at memory and runtime performance metrics and end with next steps for further improvements and optimizations.

Before we dive in, note that the Reality Tiler primarily operates on photogrammetry models or models reconstructed from LiDAR scans, potentially massive in scale. These models typically consist of explicit geometry, e.g., a triangle mesh, and color texture information.

The first version of the Reality Tiler operated by iteratively simplifying and subdividing the entire input model.

Reality Tiler v1 flow: simplification, subdivision, and tile generation.

Although the Tiler made use of out-of-core techniques to ensure that we didn’t run out of memory, it had two shortcomings. First, data access patterns were incoherent. When generating tiles or simplifying the model, the data needed for a series of operations were not coalesced into contiguous memory blocks, resulting in frequent cache misses and requiring unnecessary paging between disk and RAM. These types of access patterns can slow down processing, sometimes substantially.

The second shortcoming of that approach is that it created a fixed number of levels of detail (LODs) across the board. It is a reasonable approach for models that are relatively uniform, but some models aren’t uniform. In practice, many models have areas of higher detail and areas of lower detail. For example, parts of a model may have much higher triangle density than others or use higher resolution textures. A fixed number of LODs can create unbalanced tilesets, with some tiles containing too much information and other tiles containing too little. At runtime, this translates to inconsistent performance: Too many large tiles leads to lower framerates, and too many small tiles can result in excessive network requests and slow streaming.

Reality Tiler v1 created uniform tilesets. Notice how the tree is balanced but some tiles are larger than others.

We considered several strategies to address the shortcomings of Reality Tiler v1, but we ultimately decided to flip the entire process on its head. Rather than taking a global approach—which is to say, operating on the entire model when producing tiles and performing simplification—we take a local approach and process only one part of the model at a time. More specifically, Reality Tiler v2 splits the model into leaf tiles and builds the tileset by iteratively merging and simplifying tiles until we are left with a single root tile. Because individual tiles are relatively small and mostly contiguous in memory, data access is much more coherent, and, consequently, the overall process is significantly faster.

Reality Tiler v2 flow: subdivision, mergers, simplification, and tile generation.

With Reality Tiler v2, we ensure that each leaf tile is approximately the same size (in terms of geometry and texture content, not extent in world-space). This means that certain parts of the input model with dense geometry or texture data may be tiled more deeply than others, and tiles further up the tree remain approximately uniform in size.

Reality Tiler v2 creates uniform tiles. Notice how the tree is unbalanced but the size of each tile is the same.

By switching to this new, local tiling approach, we see dramatic improvements in tiling performance and memory usage. Here’s a look at the results for a few datasets. (All tests were run on a machine with 256GB of RAM, 64 logical CPU cores, and a 4TB NVMe drive.)

**Reality Tiler Tiling Time Comparison**

Dataset           |Triangle Count (Millions)         |Prev. Tiling Time (h:mm:ss)|New Tiling Time (h:mm:ss)|Percent Reduction in Tiling Time
---------------------|------------|----------|----------|-----------
Carrick Hill (Australia)                 |3.3|0:02:37       |0:01:11       |55% |
Liverpool (UK)|7.8        |0:08:48       |0:02:54 | 67%|
Budapest (Hungary)                 |43.1|0:59:05      |0:14:08       |76%|
Melbourne (Australia)		|69.9|3:22:41|0:27:58|86%|



**Reality Tiler Memory Usage Comparison**

Dataset           |Triangle Count (Millions)         |Prev. Peak Memory (GB)|New Peak Memory (GB)
-----------------------|-----------|---------|-----------
Carrick Hill (Australia)                 |3.3|13.5     |14.9    |
Liverpool (UK)|7.8        |22.1    |22.7 |
Budapest (Hungary)                 |43.1|61.7    |21.3|
Melbourne (Australia)		|69.9|134.5|26.9|


Reality Tiler v2 tiling time is reduced across the board, about 2-7 times faster than Reality Tiler v1. Generally, the larger the input, the greater the speedup. Memory requirement trends have similar gains, with the largest datasets seeing the greatest reduction in peak memory usage (for smaller datasets, we see a slight increase in memory usage for these samples). Taken together, these improvements indicate substantially improved scalability for the Reality Tiler: It’s able to process more data in a shorter period of time, while typically using less memory.

Because of non-uniform tiling, we also see a reduction in the variation of file sizes for tiles. Melbourne, Australia, for example, is a dataset that does not have uniformly sized triangles, and therefore it particularly benefits from non-uniform tiling.

A wireframe view of a portion of Melbourne. Notice the variation in triangle size and density: The buildings (upper left) have small and densely packed triangles, while the triangles that make up the river (lower right) are much larger.

With Reality Tiler v1, which tiles uniformly, Melbourne’s average tile size was 1.28 MB, with a standard deviation of 706 KB. For v2, the average tile size is 395 KB, with a standard deviation of 192 KB.

We’ve come a long way since the v1 release of the Reality Tiler, and we still have a number of ideas for future enhancements. The two largest bottlenecks in our pipeline are now texture compression and texture coordinate generation. We will likely focus on both in the near future.

We will also work to bring down peak memory usage even further. Although mesh data is mostly handled out of core, the data structures that help us index into it reside in memory. We plan to adapt this structure to also not entirely reside in memory.

Interested in using the updated Reality Tiler? Sign in to your Cesium ion account or sign up and get started for free!

Reality Tiler V2 Improves Tiling Time and Memory Usage

In November, we dove into the benefits of Cesium ion’s automated CI/CD pipeline and prepared for this year’s I/ITSEC in Orlando.

CesiumJS 1.124 is now available. Highlights of the release include:

Added an integration with the iTwin Platform to load iModels as 3D Tiles. Use ITwinPlatform.defaultAccessToken to set the access token. Use ITwinData.createTilesetFromIModelId(iModelId) to load the iModel as a Cesium3DTileset. #12289

PostProcessStageCollection.ambientOcclusion has been updated with a new algorithm to provide better results at all scales, with tunable performance cost. #12316

Added Entity.trackingReferenceFrame property to allow tracking entities in various reference frames. #12194, #12314

Added GoogleGeocoderService for standalone usage of Google geocoder. #12299

Fixed various performance issues related to loading many models (#12320), incorrect shared texture memory tracking (#12331), and point cloud filtering. (#12317)

An iModel of a Philadelphia Metrostation on top of an iTwin Reality Mesh shown in CesiumJS.

Cesium for Unreal v2.11.0 is now available. This is the last release of Cesium for Unreal that will support Unreal Engine v5.2. Future versions will require Unreal Engine v5.3+.

Added three new levels to the Cesium for Unreal Samples project.

Added a "From Ellipsoid" option to Cesium3DTileset to generate a tileset by tessellating the surface of the ellipsoid, producing a simple globe tileset without terrain features.

Fixed an overlay artifact bug that could occur when a Cesium3DTileset had three or more raster overlays.

Fixed a crash that could occur when using SampleHeightMostDetailed on a Cesium3DTileset with a raster overlay.

CesiumPolygonRasterOverlay now references CesiumCartographicPolygon instances using TSoftObjectPtr, which allows, for example, a raster overlay in the persistent level to use a polygon in a sub-level.

Snowdon Sample Model from Autodesk Revit, streamed with 3D Tiles and visualized using Cesium for Unreal. Featured in Level 14_CesiumArchitecturalDesign in Cesium for Unreal Samples.

Metadata queried from a door component in Snowdon Sample Model. Featured in Level 14_CesiumArchitecturalDesign in Cesium for Unreal Samples.

Metadata styling to highlight HVAC components in Snowdon Sample Model. Featured in Level 15_CesiumArchitecturalMetadataStyling in Cesium for Unreal Samples.

The same tileset with isolated floor views. Featured in Level 16_CesiumArchitecturalIsolateCategory in Cesium for Unreal Samples.

Cesium for Unity v1.14.0 is now available. Highlights of this release include:

Fixed a bug in CesiumWebMapTileServiceRasterOverlay that caused automatic Tile Matrix Set Labels to be created incorrectly, leading to incorrect tile request URLs.

Coming soon: full release of the Design Tiler and Cesium ion for Autodesk Revit Add-In.

Fixed a crash that would sometimes occur when subdividing model data to produce leaf tiles.

Fixed incorrect unlit and double-sided materials settings for generated tilesets with a single tile.

Fixed an issue where percent-encoded URIs were incorrectly decoded.

Fixed a rare issue where some tiles would have abnormally large textures.

Added experimental support for Reality Capture and iTwin Capture sidecar files.

Added support for embedding environment variables in paths within the config file (e.g., ${ENV_VAR}/path/to/model.obj).

Improved texture generation for some tiles.

CalTa Inc. has developed TRANCITY, a platform for managing large-scale infrastructure projects such as railways, roads, rivers, ports, and power plants in a three-dimensional space, with Cesium’s platform playing a crucial role in its operation.

COMSPOC has released Spacebook to make space data free to access and visualize with the help of CesiumJS.

CalTa株式会社では、鉄道を中心に、道路、河川、港湾、発電所などの大規模なインフラ施設の現場管理業務を三次元空間上で行う「TRANCITY」を開発しており、その処理にCesiumのプラットフォームが活用されています。

TRANCITYは、ドローンやロボットで撮影した動画だけでなく、スマートフォンやタブレット端末等で撮影した動画もアップロードするだけで、三次元地理空間内の現実空間と一致する３Ｄモデルがスピーディーに自動生成されます。動画のほか、BIMモデルや点群データのアップロードも可能であり、3Dモデルと重ねて表示することができます。また、TRANCITYはブラウザベースのためPC、タブレット、スマホ等どんな端末でも活用することができます。３Ｄモデルは時系列ごとに管理され、位置情報と紐づくことで「いつ、どこが、どうなっているか」を正確に把握することが可能です。TRANCITYを活用することで、オフィスと現場等の離れた職場での詳細な情報共有が可能となり、東日本旅客鉄道株式会社様や東海旅客鉄道株式会社様などの鉄道業界をはじめ、インフラ業界における生産性向上や、持続可能なインフラ管理をサポートしています。

TRANCITYで扱う発電所工事施設内、施設建設予定地内の多種大量の３Dデータを最適な形に３Ｄタイル化、かつ高速にストリーミングし、デバイスを問わずスムーズな可視化を実現するのが CesiumJS と Cesium ionです。 3D タイルは、Cesiumによって作られた、点群や建物、フォトグラメトリなどの大規模で異質な3D地理空間データセットのためのオープン標準です。TRANCITYでは、鉄道などの大規模インフラの管理情報と位置情報を結びつけることで効率化されますが、それらの3DデータはCesium ionを用いて3Dタイルとして最適化され、あらゆるデバイスのCesiumJSに高速にストリーミングされます。 TRANCITYでは、まず動画データからSfM技術を用いて3Dデータを生成し、生成したデータを3D Tiles形式にタイル化してビューワに表示しています。この一連の工程を通じて、約1時間以内でデータの可視化が可能です。このプロセス全体には、Cesium ionエンジンの高品質な処理能力が大きく貢献しています。

ストリーミングされたデータは、CesiumJSを使用してWGS84座標系に基づいて正確に三次元地理空間上に表現され、ウェブ上で可視化されます。このようにCesiumJSは、多様なユーザーエクスペリエンスを実現するためのライブラリとして活用されています。

TRANCITY上で再現された構造物の距離計測

TRANCITY上で再現された鉄道現場での面積算出

Trancity (Japanese)

Cesiumは海外諸都市ですでに多くの利用実績があり、CityGMLやLASといった様々な3Dデータ形式を3Dタイルに変換しストリーミング用に最適化しています。そのオープンな開発環境により豊富な開発事例を保持していることや、Web上で可視化され誰でも利用できる点、などが評価され採用されました。

ここで東日本旅客鉄道株式会社様と東海旅客鉄道株式会社様の実際の活用事例をご紹介します。

東日本旅客鉄道株式会社様では、発電所の更新工事において、以下のような課題を抱えていました。

広大な敷地内に展開されるインフラ工事の監理を少人数で行わなければならない

設計モデルとの整合性を確認するために各所寸法を計測する際、担当者が都度オフィスから離れた現場へ移動しなければならず、往復4時間かかる

また、東海旅客鉄道株式会社様では、鉄道の線路上空を交差する橋梁の点検において、以下のような課題を抱えていました。　　

終電から始発までの限られた時間の中で線路内に足場設置、点検、足場撤去をしなければならず点検の時間が十分に取れない

危険な作業が多く墜落などのリスクがある

これらの課題解決に向け、東日本旅客鉄道株式会社様においては、発電所工事の動画をTRANCITYへアップロード。現場が3Dタイルで正確かつ効率的にモデル化され、Webブラウザを通じて、三次元地理空間上で現場の詳細な状況を把握でき、少人数での効率的な管理が可能になりました。併せて現場とオフィスを移動する足ロスも解消することができました。

また、東海旅客鉄道株式会社様では橋梁の点検現場を３Ｄモデル化し、三次元地理空間上で点検を行うことで、生産性向上と安全性の向上を図ることができました。

＜東日本旅客鉄道株式会社様 　現場で計測していた業務をPC上で実施するイメージ＞

＜東海旅客鉄道株式会社様　夜間に足場を設置していたものを動画撮影に置き換えるイメージ＞

東日本旅客鉄道株式会社様、東海旅客鉄道株式会社様は、施設・設備工事の調査・設計・施工・点検管理からステークホルダーとの合意形成等に至るまで、労働者不足という日本社会に共通する大きな課題の解決を目指し、Cesium利用によるTRANCITYを活用した大幅な効率化や、分かりやすい説明によるステークホルダーの満足度向上に取り組んでいます。     

上記の改善点以外にも、従来、３Dデータを取り扱うために必要だった高性能な端末を不要にし、スマートフォンやタブレット端末などのツールでデジタルツイン上での速やかな点検や計測業務、シミュレーション提示を可能にするなど、デジタルツインのデータによる全く新しいサスティナブルな働き方への変革を実現しています。

TRANCITYは、日本のものづくりに求められる厳しい品質基準を満たすために、Cesiumでの正確な地球上の位置座標の表示性能を活用しつつ、質の高いデジタルツイン空間を実現しています。例えば現場の一部である鉄筋組み立て配置状況が、当該現場全体を空から俯瞰した状態からアクセスできるようになります。このため、鉄道のような線状に長い現場や、工場のように面状に広大な現場であっても、個々の細かな現場変化を効率的かつスピーディーに管理することが可能となりますが、これをCesiumとの連携により実現しています。すなわち、ミクロとマクロがシームレスかつ正確につながり、マクロの現場状況を地球レベルでの現場状況から拡大して確認できるデジタルツイン空間を実現したものです。それは、これまでに存在しない仕組みであり、人間にとってより身近なデジタルツイン空間を実現したプロダクトと言えます。そして2024年10月、TRANCITYはCEATEC AWARDデジタル大臣賞を受賞しました。

ご自身のインタラクティブなウェブアプリをCesiumJSを使用して構築するには、当社のラーニングセンターガイドをご参照ください。Cesium ionを使用すれば、全世界の地形、画像、建物を含むフルスケールの地球モデルに独自のデータをインポートすることができます。Cesiumの3Dタイルパイプラインは、データセットのサイズに関係なく、データをストリーミングに最適化します。無料のCesium ionアカウントにサインアップし、

Cesium Storiesを使って簡単に3D地理空間の体験を作成できる方法をお試しください。

また、Cesium ionの SaaSや セルフホステッドのソリューションがどのように貴社の3Dアセットの価値を引き出すのに役立つか、については、sales@cesium.comまでお問い合わせください。

TRANCITY Uses Cesium to Manage Japanese Railway Projects

CalTa Inc. has developed "TRANCITY," a platform for managing large-scale infrastructure projects such as railways, roads, rivers, ports, and power plants in a three-dimensional space, with Cesium’s platform playing a crucial role in its operation.

TRANCITY swiftly and automatically generates 3D models that accurately reflect real-world conditions in a 3D geographic space from various types of video footage. This includes videos captured by drones or robots, as well as those taken with smartphones or tablets. Additionally, users can upload BIM models and point cloud data, which can be overlaid on the 3D models. As a browser-based solution, CesiumJS-powered TRANCITY is accessible from any device, including PCs, tablets, and smartphones. The 3D models are managed chronologically and linked with location data, allowing precise tracking of "what happened, where, and when." By using TRANCITY, detailed information sharing between remote offices and field locations is facilitated, thereby supporting productivity and sustainable infrastructure management in the railway industry and beyond, including companies such as East Japan Railway Company and Central Japan Railway Company.

Because TRANCITY is built on CesiumJS and Cesium ion, it can efficiently handle and stream large volumes of diverse 3D data from power plant construction sites and planned facility construction areas, by converting them into optimized 3D Tiles for smooth visualization across all devices. Created by Cesium, 3D Tiles is the open standard for massive, heterogeneous 3D geospatial datasets such as point clouds, buildings, and photogrammetry. TRANCITY enhances the management of large-scale infrastructure by linking 3D data with location information, and Cesium ion optimizes this data into 3D Tiles that are streamed rapidly to CesiumJS on any device. TRANCITY generates 3D data from video footage using their SfM technology at first, then transforms the data into 3D Tiles format for display in a viewer. This entire process enables data visualization in under an hour. The high-quality processing capabilities of the Cesium ion engine play a crucial role in this workflow.

The streamed data is accurately represented in 3D geographic space based on the WGS84 coordinate system and visualized on the web using CesiumJS. CesiumJS is utilized as a library to enable a variety of user experiences.

Distance measurement of structures recreated on TRANCITY. Courtesy CalTa Inc.

Area calculation at the railway site recreated on TRANCITY. Courtesy CalTa Inc.

Overview of TRANCITY. Courtesy CalTa Inc.

Trancity (English)

Cesium has extensive usage worldwide, having demonstrated its capabilities in numerous cities. It converts various 3D data formats, such as CityGML and LAS, into 3D Tiles optimized for streaming. The elements like the platform's open development environment, extensive development case studies, and capability for web-based visualization accessible to anyone were highly regarded and led customers to select Cesium.

Here are the actual cases of how East Japan Railway Company and Central Japan Railway Company have utilized TRANCITY and Cesium:

East Japan Railway Company faced the following challenges during the upgrade of a railway-owned power plant:

Managing infrastructure work across a vast site with a small team.

Checking measurements for consistency with design models required staff to travel to the site from the office, taking four hours for a round trip.

Central Japan Railway Company encountered the following difficulties with bridge inspections over railway tracks:

Installing scaffolding, inspecting the bridge, and removing scaffolding within the limited time between the last and first trains, which constrained inspection time.

The work involved significant risks, including potential falls.

East Japan Railway Company uploaded videos of the power plant construction to TRANCITY. This allowed the site to be streamed as accurate, efficient 3D Tiles, providing detailed situational awareness in a 3D geographic space via a web browser and enabling efficient management by a small team. It also eliminated the need for frequent site visits, reducing travel time.

Central Japan Railway Company used TRANCITY to model the bridge inspection site and stream it as 3D Tiles to the web, enhancing productivity and safety by conducting inspections within a 3D geographic space instead of short nighttime visits.

Before: On-site visits. Courtesy East Japan Railway Company. 

After: East Japan Railway Company uses TRANCITY and Cesium to measure and perform other tasks on a PC that previously required on-site visits. Courtesy CalTa Inc.

Video recordings can be added to TRANCITY instead of risky nighttime inspections in a short time. Courtesy Central Japan Railway Company.

East Japan Railway Company and Central Japan Railway Company are leveraging Cesium-powered TRANCITY to significantly enhance efficiency in addressing Japan's widespread labor shortage, a challenge that spans across various sectors of society. Their efforts encompass everything from surveying, designing, construction, and inspection management of facilities and infrastructure, to fostering consensus among stakeholders through clear and comprehensible explanations aimed at improving stakeholder satisfaction.

Beyond these improvements, CalTa Inc. is also pioneering a transformative approach to sustainable work practices, enabled by digital twin technology. This new method eliminates the need for high-performance devices traditionally required to handle 3D data, allowing for swift inspections, measurements, and simulation presentations on digital twins using smartphones, tablets, and other portable tools.

TRANCITY meets the stringent quality standards demanded by Japanese manufacturing by utilizing Cesium's precise geospatial capabilities to create a high-quality digital twin environment. For instance, TRANCITY allows access to detailed views of rebar assembly configurations within a site from an aerial perspective. This capability facilitates efficient and rapid management of both linear sites, such as railways, and extensive areas like factories, by seamlessly integrating detailed micro-level changes with macro-level site conditions on a global scale. This represents an unprecedented system that delivers a more accessible digital twin experience for users, and in October 2024 TRANCITY received the CEATEC Digital Minister’s Award for this work.

To build your own interactive web applications using CesiumJS, please refer to our Learning Center guides. With Cesium ion, you can import your data into a full-scale global model that includes worldwide terrain, imagery, and buildings. Cesium's 3D Tiles pipeline optimizes your data for streaming, regardless of dataset size. Sign up for a Cesium ion account and explore how to create immersive 3D geospatial experiences. For more information on how Cesium ion's SaaS or Self-Hosted solutions can enhance the value of your 3D assets, please contact us.

TRANCITYはCesiumを使用し、日本の鉄道インフラの管理を実現

Cesium ion SaaS handles a wide range of functions and serves a global user base, requiring rapid updates and bug fixes. To deliver at this pace, Cesium minimizes the time developers spend away from feature development. An automated testing and deployment pipeline is key: It allows developers to focus on adding value for users while automation handles the operational aspects of deployment.

Although setting up an automated CI/CD (continuous integration/continuous deployment) pipeline involves a large up-front investment in engineering resources, the long-term benefits are substantial. The efficiency gained through reduced development friction and accelerated deployment far outweighs the initial cost. In this blog, we share Cesium ion’s automated CI/CD pipeline, which closely follows AWS’s recommended practice and enables several deployments a day. This enhances our ability to deliver consistent value to our users.

The first step is to run automated tests, which include unit and end-to-end tests, to ensure the new code is up to our engineering standards. Our CI infrastructure, powered by Github Actions, is responsible for running these tests for every commit on all repositories that are part of ion. Beyond testing, GitHub Actions also handles code containerization, linting, and automated security scanning. The extensive coverage of these automated tasks enables us to ensure each commit is a deployment-ready update to Cesium ion.

Once the above CI checks pass on a pull request, and the code has been through a pull request review process, the next step is to execute our CD pipelines. Cesium ion operates three deployment environments: development, staging, and production. We manage these environments with two distinct CD pipelines. We use a branching strategy for our repositories, in which the main branch always contains the latest changes, while the production branch represents what has been deployed. Changes to the development environment are automatically deployed with every merge to the main branch, providing us with an always up-to-date environment to test the latest functionality. Because the CI directly kicks off the development environment pipeline, it is easy to point the development pipeline to a feature branch and deploy it to do testing in a realistic environment before merging the code to main. The second pipeline manages deployments to staging and production. It is triggered automatically anytime a commit is made to the production branch in any one of our repositories. See the graphic below for details about how these pipelines deploy:

Deployment pipeline for infrastructure and application code for Cesium ion SaaS.

The containerized images from the above CI steps are pushed to AWS Elastic Container Registry (ECR) for deployment. In the development environment, these images are directly pushed to the development AWS account. For staging and production environments, the images are pushed to a “SharedServices” AWS account, which manages cross-account permissions and deployment processes. 

All of the infrastructure within our AWS accounts is stored in AWS CloudFormation templates (Infrastructure as Code). The CD pipeline also updates various AWS components, including policies, alarms, notification topics for services like RDS PostgresQL, Elastic Container Service (ECS) clusters, Route 53, AWS WAF, CloudFront, and AWS Batch. These CloudFormation templates are deployed to the respective AWS account through the CI in the infrastructure repository. Updates to these container images in ECR or CloudFormation templates trigger an AWS CodePipeline execution. The pipeline uses the new images to update the AWS ECS cluster and the CloudFormation templates to update policies and infrastructure settings. The pipeline first applies necessary infrastructure updates before deploying the updated images to AWS ECS. We have automated rollbacks and alerts configured to address any issues if a new container instance fails to start. 

The development and staging pipeline, managed by “SharedServices,” first pushes all updates out to staging, and then, as a second step, performs the updates in production. For quality control, we have included a review step between the staging and production deployments, though our setup allows for automated production deployment if necessary. In addition to managing container images and infrastructure updates, our CD pipeline performs several other tasks. For our front end, which consists of statically hosted files, the pipeline handles compilation and transpilation during CI, with the files then pushed directly to the respective accounts. For our AWS Lambda and Lambda@Edge functions, the pipeline generates a hash based on the function code and compares it with the existing production version. If there is a mismatch, the updated lambda function is pushed when the pipeline is triggered. The primary reason for doing deployments only when we detect code changes is to speed up overall deploy time.

With our streamlined process, deploying changes to Cesium ion SaaS is extremely easy. Developers simply need to merge changes to main and production branches of the respective repositories, click a button, and make the feature available to millions of users worldwide.

Experience features from the advanced pipeline by signing up or signing in to your Cesium ion account.

In October, we discussed our collaborations with Esri, highlighted AERO AI’s work on reintegrating WebODM with Cesium ion, shared progress on the Cesium Revit Add-In, introduced Japan Regional Terrain and updates to Japan 3D Buildings, dove into how to become a Cesium Certified Developer in Japanese, and explained key updates to glTF rendering in CesiumJS.

CesiumJS 1.123 is now available. Highlights of the release include:

Updated default 3D Tiles and Model lighting when using PBR in order to create a more realistic appearance. #12129

Added ScreenSpaceCameraController.maximumTiltAngle to limit how much the camera can tilt. #12169 by new contributor albek446

Updated Japan Buildings sandcastle to use Japan Regional Terrain. #12259

Moved Viewer functionality to CesiumWidget to increase usability. Makes the CesiumWidget closer to a drop in replacement for the Viewer when not utilizing the extra Viewer widgets. #11967.

Fixed flickering issue caused by bounding sphere retrieval being blocked by the bounding sphere of another entity. #12230

Updates to default 3D Tiles and Model lighting when using PBR create a more realistic appearance.

Cesium for Unreal v2.10.0 is now available. Highlights of the release include:

Added a shared assets system that ensures external images referenced by different glTFs will only be loaded once per image. Previously, these images would be loaded again for each glTF that referenced them. This significantly reduces memory usage for tilesets that reuse the same textures.

Added CircumscribedGroundHeight property to CesiumSunSky. It defaults to 0, which is consistent with the previous behavior. It can be set to a larger value (like 40) to avoid dark splotchy artifacts when zoomed out far from the globe in certain tilesets where geometry extends very far beyond the ellipsoid in the low-detail representation, such as Google Photorealistic 3D Tiles.

Fixed a bug that caused incorrect lighting for tilesets using KHR_materials_unlit.

Reduced the memory used by tiles with KHR_materials_unlit.

CesiumGlobeAnchor properties are no longer shown on the main CesiumSunSky Details panel, because it is almost never necessary to set these. They can still be set on the component's own Details panel if needed.

Fixed error messages in the Unreal log about uninitialized fields in FCesiumSampleHeightResult.

Google Photorealistic 3D Tiles captured from an outer space view, after improvements to its generated normals and surrounding atmosphere.

Cesium for Unity v1.13.1 is now available. Highlights of this release include:

Cesium Releases in February 2025

Cesium Releases in January 2025

Reality Tiler V2 Improves Tiling Time and Memory Usage

Cesium Releases in December 2024

TRANCITYはCesiumを使用し、日本の鉄道インフラの管理を実現

Cesium ion’s CI/CD Pipeline

Cesium Releases in November 2024