IoT, UAV & Drones
Problem Use Case
- Canopus collaborated with a mid-sized company based out of Belgium who works in the drone surveying and performing GIS and Geo analytics on the data captured utilizing the drone flights.
- Client wanted to perform following analysis on the LiDAR sensor data captured from the drone flight over
- Geo-position of Christmas trees and geo-position per hectare of Christmas trees.
- Height of Christmas trees.
- Volume of Christmas trees
- Lidar (light detection and ranging) is an optical remote-sensing technique that uses laser light to densely sample the surface of the earth, producing highly accurate x,y,z measurements. Lidar, primarily used in airborne laser mapping applications, is emerging as a cost-effective alternative to traditional surveying techniques such as photogrammetry.
- Data and challenges: The data consists of point clouds in .las format collected using Lidar technology.
- Challenges: As the point cloud was not classified into ground versus non-ground and vegetation classes which is required to generate DTM, DSM and calculation of height, volume of trees.
- Applied machine learning techniques to classify point cloud into ground and vegetation classes.
- From classified points we generated DTM(Digital Terrain Model) and DSM(Digital Surface Model) and CHM(Canopy Height Model).
- Using the CHM, calculated the height and volume of trees from sampled data