Classification of LiDAR point cloud

A LiDAR point cloud has been classified in WiMap BM. Each connected "unit" was assigned a unique color. The colors were assigned to an image and a grid/raster/DSM of the point cloud was textured with the image.

Building models from LiDAR point clouds

3D LiDAR point cloud, part of Stockholm, Copyright: Blom Sweden AB

I have started working on WiMap BM again. I have been documenting the code and planned for future development. It takes some time since it has been a while since I worked with WiMap BM. The system is 20% faster when the data is on my SSD than when the data is on the standard hard disk.

Segmentation with Solid State Drive

I bought a Solid State Drive (SSD) to check how much faster it is than my standard hard disk drive. The SSD is an Intel X25-V. I tested an image segmentation of a panchromatic 16-bit satellite scene of more than 200 Mb. Running a segmentation with my standard hard disk drive required 4 min 35 sec. Then I changed the path for the temporary files to the SSD drive, since the image caching uses temporary files, and I copied the image to the SSD drive and finally set the output directory of the segmentation file to the SSD drive. I guess I should have moved the program to the SSD drive as well, but that shouldn´t make much difference. The segmentation now required 4 min 11 sec - 9 percent less. Most of the processing is of course CPU intensive rather than file intensive, so I think the result is quite ok. I will at least permanently let the temporary folder be on the SSD disk.