GIS & Geospatial
Development of a Web-Based 3D Point Cloud Visualization Platform
The client wanted engineering and geospatial teams to be able to open and work with high-volume point cloud datasets without a heavy desktop install.
- Industry
- GIS & Geospatial
- Location
- Saudi Arabia
- Team
- Software engineers, 3D visualisation specialists, and cloud infrastructure engineers. A project manager ran development on an agile cycle, with the frontend (Angular, Potree) and backend (Python) teams working closely. A separate QA team tested performance, accuracy, and stability across datasets and use cases.
- Stack
- Python, Django, Celery - Angular, Leaflet - laspy - Docker
About the client
Engineering and Surveying Solutions Provider (Saudi Arabia)
The challenge, and what closed it
Technical challenge
Massive Data Volume: rendering trillions of points in a browser without losing metadata or visual fidelity. Interactive Performance: measurement and analysis tools that respond immediately, with no lag between input and result. Security: fine-grained access control and secure delivery for high-value geospatial data.
What we did
A web application for viewing, managing, and working with massive 3D point cloud datasets. It handles LIDAR, photogrammetry, and other 3D formats, and users measure, annotate, clip, and export data in the browser. We built the 3D viewer on Potree and Angular, with a Python backend and SQL Server for metadata. The PotreeConverter pipeline turns raw LIDAR into an octree format, which is what makes loading near-instant and interaction smooth. The platform carries user authentication, role-based access, volume measurement, height profiling, clipping tools, and layer-based visualisation, in a UI that runs in any WebGL-enabled browser.
Why us
The development process included: Requirements gathering and solution architecture planning; Integration of Potree and PotreeConverter for high-performance rendering; Backend development using Python and SQL Server; Frontend development using Angular and WebGL for interactive tools; User authentication with JWT and secure role-based access; Cloud-agnostic deployment (support for AWS, GCP, and on-prem); Stress testing with large-scale datasets; Deployment with NGINX and secure storage configuration.
Results
The Point Cloud platform let the client offer cloud-delivered 3D visualisation to surveyors, GIS analysts, and engineers. It took heavyweight desktop software out of the workflow and made complex datasets easier to reach. It is now used in city planning, environmental monitoring, infrastructure inspection, and academic research, by both public and private bodies.
What we delivered
A web application for viewing, managing, and working with massive 3D point cloud datasets. It handles LIDAR, photogrammetry, and other 3D formats, and users measure, annotate, clip, and export data in the browser.
Tech stack
- Backend
- Python, Django, Celery
- Frontend
- Angular, Leaflet
- Data & AI
- laspy
- Infrastructure
- Docker
