Enriching 3D point cloud data sharing with artificial intelligence for Asset.Insight.

To make VolkerWessels' 3D point cloud data accessible across the company in a secure way, we partnered with Asset.Insight. to build a scalable Azure-based data platform, paving the way for advanced analytics.

We have known Xomnia for years now, and from the onset we have only had good experiences with the experts from Xomnia.
Steven Woudenberg, Manager Advanced Analytics, Asset.Insight.


Asset.Insight. is the data service provider for VolkerWessels, a Dutch construction company aiming to create sustainable, innovative and future-oriented living environments. Like most construction companies, VolkerWessels is increasingly relying on 3D point cloud data for accurate and fast records of the 3D geometries within their projects.

However, this data is often being stored on hard drives and used for a single goal. By migrating to cloud infrastructure, VolkerWessels would make data sharing easier among their 120 local operating companies worldwide and increase the searchability and security of their data.

The stored data could also help VolkerWessels companies to advance their asset management. For example, they could predict potential equipment failures. Since they manage assets related to rail, roads, and water, this would increase both the availability of the infrastructure and the safety for company employees and for the general public.

Asset.Insight. created a proof of concept for the project and turned to Xomnia to help turn it into a scalable solution. In order to save time and prevent duplicate work, the challenge was to make this vast amount of data easily searchable and convenient to use.


In collaboration with the Asset.Insight. team, we created a platform for users within and outside of VolkerWessels to safely store large 3D datasets. Through a web portal as well as APIs, users can search through these data sets and can download partial or complete files.

As a next step was we enabled the user to not only download 3D point clouds, but also to apply state-of-the-art algorithms on the point clouds on the platform. For example, if a client would like to know the location and height of all trees in an area, the user can select that area and the an algorithm for estimating tree height is applied on the available 3D point cloud data in that area.

With Azure Active Directory integration, clients can seamlessly use the same credentials they use within their company to authenticate to Asset.Insight.’s services. Azure Data Lake Storage is used to provide a scalable and fault tolerant storage solution with isolation between each clients’ provisioned storage.

The various processing tasks to be carried out are scheduled on a Kubernetes cluster for scalability and efficient use of resources. We utilized Cesium and their 3D tiles standard in order to display available data in 3D, overlayed on existing maps and terrain models.


The immediate business impact is two-fold. Sharing the 3D point clouds through a platform, which is always accessible, enables the clients to easily find and re-use the 3D point cloud data for all projects. The solution does not limit itself to 3D point clouds; any type of file can be uploaded and made available. Furthermore, clients can have easy access to the results of algorithms that are applied on the uploaded data.

At the moment, Asset.Insight. is attracting more users to the platform to test it and store their data. Once the product is fully developed, it will be possible to utilize the data to accurately predict asset failures. The state of the art algorithms used for this can then be integrated into the platform. This has enormous potential for further future-proofing Asset.Insight.