Matom.AI Extracting AI Insights from Point Clouds in CesiumJS
Matom.AI is applying AI to city‑scale point clouds, extracting meaningful insights from complex geospatial data for city management, power line inspections, and forest analysis. A Cesium Certified Developer, Matom.AI uses CesiumJS to build digital twins from massive datasets, including custom terrain, CityJSON, photogrammetry, orthophotos, and point clouds. The real innovation of Matom.AI’s platform is their AI‑driven approach to analyzing this data.
Matom.AI uses AI tools to segment point clouds and identify common elements such as trees and lamp posts. Here, they’ve automatically classified and modeled urban assets in a digital twin of Kaunas, Lithuania. Courtesy Matom.AI.
Because CesiumJS and 3D Tiles make massive geospatial datasets easy to stream and visualize, Matom.AI can apply AI directly to raw city data, eliminating the need for labor-intensive modeling and enabling scalable, continuous analysis.
While a traditional digital twin requires developers to manually clean data, hand-build models, simplify, and then visualize, Matom.AI has flipped this order. They bring point clouds directly into CesiumJS and visualize first, then analyze at scale and structure with AI. These digital twins make it possible for teams in different locations to work with complex geospatial data directly in the browser, where they visualize large‑scale 3D datasets in a unified, high‑performance environment.
By fusing datasets captured at different times and from different perspectives, Matom.AI’s digital twins provide a more complete view. Cesium enables these diverse data sources to be visualized seamlessly within a single platform. Streamed in the browser as 3D Tiles, the data supports shared workflows, custom analysis, and reporting. The platform also supports real-time data collection through a mobile app, allowing field teams to capture information and add updates directly to the digital twin.
Matom.AI’s platform integrates AI-driven point cloud classification and instance segmentation, automatically detecting benches, poles, traffic signs, trash cans, and other urban assets. Each object can be analyzed individually and converted into structured models, making visualization and inspection more efficient. Built-in tools also calculate core characteristics of each element, such as location, height, and width, simplifying inventory management and infrastructure assessments.
AI-powered image segmentation, combined with measurement analysis tools, provide quick insight into large datasets, as in this detail view of a bus stop in Kaunas, Lithuania. Courtesy Matom.AI.
One of the most valuable capabilities Matom.AI provides is AI-powered change detection. By comparing scans captured at different points in time, the system highlights changes in buildings, vegetation, and city assets, helping users identify issues earlier and prioritize maintenance before small problems grow into larger challenges.
Here, Matom.AI highlights discrepancies between two datasets: one captured in winter and the other in summer in Kaunas, Lithuania. Courtesy Matom.AI.
By making point clouds easier to analyze and act on, Matom.AI enables cities to collect data more frequently and maintain a continuously up‑to‑date digital twin. To visualize and analyze your point clouds in a geospatial environment and easily share with colleagues, bring them into CesiumJS using 3D Tiles.
If you are building creative solutions with Cesium, consider applying for the Cesium Certified Developer program, an opportunity to demonstrate your expertise and connect with other developers in the community.