Recent decades have seen rapid growth in the application of LiDAR in various industrial domains. LiDAR sensors are becoming more and more available and currently can be attached to any device from a drone to a transport vehicle. This ensures fast and easy high-precision data collection. Yet, the most challenging and time-consuming task is to process the data accurately and deliver valuable insights to the final customer.
EOS is undertaking a mission of interpreting and analyzing big data for a diversity of your needs. Starting now, with the advanced algorithms provided by our data scientists team, you are able to get the most accurate results.
We detect human-made objects/artificial structures and create accurate 3D urban models from raw LiDAR Point Clouds data in a fully automated mode. As a result, raw point cloud input data is transformed into 3D vector models with a high level of detail.
The system enables a monitoring of urban architecture changes over a selected period of time. It is done via an automated comparison and analysis of two LiDAR Point Clouds. This solution helps detect the locations of buildings change (position and shape) and present them as 3D models with the information about the changes.
The system builds a Digital Elevation Model that represents the bare earth terrain of a selected area, which is essential while managing various types of projects. It helps accurately and precisely visualize the contours and get almost a real picture with all the natural and human-made objects within the area.
All files within input datasets should have the same coordinate reference system
Point density should be more than 3-5 points per square meter
Real-time security perimeter monitoring
The surveillance solution is highly effective for any land monitoring and may be deployed in the worst visibility, including dust, smoke, fog, among other conditions. The software can be employed as a stand-alone monitoring system or integrated with other existing surveillance techniques such as a Video Surveillance System.