LiDar system is emerging as a cost-efficient alternative to conventional surveying techniques like photogrammetry. It is an active optical sensor technology that transmits laser beams toward a target while moving through a defined route. However, the increased utilization of LiDar produces a terabyte of lidar data that is a difficult task to manage.
A LiDar system comprises major hardware components including a collection vehicle, laser scanner system, GPS (Global Positioning System), and internal navigation system (INS). As lidar transmits laser beams towards a target, the reflection of the laser from the target is detected and assessed by receivers in the lidar sensor. These receivers also record the exact time from when the laser pulse left the system to when it is returned to scale the range distance between the sensor and the target.
Integrated with the GPS and INS, these distance measurements are altered to analyses of actual 3D points of the reflective target in object space. For most organizations, managing lidar data is part of their larger data management effort that consists of elevation data. Almost all lidar projects need to derive elevation surfaces from 3D points, such as bare-earth digital terrain models (DTMs) and first-return digital surface models (DSMs), according to Esri. Within an organization, the elevation data can often come from several different projects.
After lidar has been processed into rasters, end-users typically require fast access to the data that can visualize elevation surfaces as a hillshade, or practice the raster surfaces for quick viewshed and volumetric analysis.
LiDar pipeline can also be managed through a cloud-enabled framework. Several businesses glean large collections of lidar data, but they face problems to keep track of what they have and providing to their end-users in an actual way.
The volume of the lidar survey increases day by day and this sheer amount of data is overwhelming with the most actionable data that typically left out. Managing lidar data generally encompasses four steps including maintaining vital insights in the database, developing a spatial structure that makes it easy to fetch data, lidar visualization and data analytics.
Advancements in computing systems have enabled the creation of very dense point clouds to store and deliver LiDar data. Commonly, lidar data is collected in terabytes in size that includes billions of points and it is often stored in a LAS file, a binary format that stores the x, y, z coordinate. This format is extensively adopted and leveraged throughout the industry. So, lidar is predicted to continue to become a key remote sensing solution for giving situational and geographical information to both public and private domains.