LIDAR technology has been widely used in recent years for good reason. Using this technology we can get higher-quality results than traditional photogrammetric techniques for lower cost. But switching to LIDAR technology is still challenging. A major challenge is increasing data volume, and volume of data is increasing day by day. So how to you use this massive data for our benefits ? Following are different ways to take charge of LIDAR data.
1. Translate one format to other:
LiDAR data is not limited to a single format. Mostly observed formats are .las, .laz, .zlas, .ep7, .pod, .xyz, .bin and many more. The most popular format is .las. LAS, short for LASer, is the industry standard format for lidar. The specification is maintained and published by the American Society of Photogrammetry and Remote Sensing (ASPRS). It’s binary, efficient, widely supported. Though LAS format is famous in industry, modern data processing requires flexibility. You need to be able to work in a variety of formats, translating not only “point cloud to point cloud”, but also “point cloud to anything” and “anything to point cloud”.
2. Reduce Lidar data:
Thinning a point cloud reduces its Data volume; for example, by removing every Nth point. By reducing data we can speed up processing time. I use Voxel grid filter from PCL library to reduce data.
3. Filter Lidar data:
Fitering Data we can extract only meaningful data and remove outliers in the data. One way to do this id, use statistical outlier removal filter of PCL library, or one can use Passthrough filter of same library to reduce data and to crop area of interest.
4. Clipping Lidar data within polygon:
Using pass through like filter its difficult to crop exact are of interest in point cloud. We can crop area in the polygon or outside the polygon using QHULL library.
Using pass through like filter its difficult to crop exact are of interest in point cloud. We can crop area in the polygon or outside the polygon using QHULL library.
5. Classify Lidar Data:
We can filter lidar data into different classes like ground, non-ground, building, low vegetation, high vegetation, medium vegetation, water, tree etc. One can use tools like Lastools but its free version has limited functionality.
6. Convert Point cloud to surface model:
Some times we are interested in topography than actual point cloud, then we can make a 3D surface model out of LiDAR data.Point cloud to DEM (Depth elevation model), Point cloud to DSM (Depth Surface model) are the mostly required tasks for GIS applications.
Some times we are interested in topography than actual point cloud, then we can make a 3D surface model out of LiDAR data.Point cloud to DEM (Depth elevation model), Point cloud to DSM (Depth Surface model) are the mostly required tasks for GIS applications.
7. Tiling Lidar Data for fast Processing:
Tiling is a process of chopping the point cloud into a series of tiles. If we have a big point cloud to do lot transformations then we can do tiling first and then after we can use parallel processing to speed up the operation.
8. Change coordinate system:
Lidar data comes with different coordinate systems like (UTM, StatePlane, etc). Before processing we require to transform into original coordinate system Georeferencing a point cloud would allow us to select control points that link locations in the point cloud with geoferenced locations and then we can transform the point cloud in desired coordinate system.
Lidar data comes with different coordinate systems like (UTM, StatePlane, etc). Before processing we require to transform into original coordinate system Georeferencing a point cloud would allow us to select control points that link locations in the point cloud with geoferenced locations and then we can transform the point cloud in desired coordinate system.
9. Split LiDAR data by by point features:
10. Region Growing
11. Object segmentation
*Note- The post is not complete yet, I will update remaining part in upcoming days.
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