Datasets are automatically trained and evaluated with OpenPointClass and the latest AI models can be downloaded from the releases page.
The resulting models are used to improve the automated classifier in ODM.
We recommend to process an image dataset with ODM or WebODM and turn on the pc-classify
option, which will automatically assign classification values to a point cloud. Some will be incorrect, but it's easier than starting from scratch.
Once you have generated a point cloud (odm_georeferenced_model.laz
), you can import it in CloudCompare. Use the latest stable release, not the alpha versions.
Then:
Properties > Scalar field > Classification
.If you are starting from an unclassified point cloud you can initialize the classification values by going to Edit > Add scalar field > Classification
Start classifying/cleaning the point cloud by going to Edit > Segment
(press T)
Draw a polygon around the points you want to classify. Right click closes the polygon.
Press C to assign ASPRS LAS codes:
At a minimum, the point cloud should have the following classification codes:
Class | Number | Description | |
---|---|---|---|
ground | 2 | Earth's surface such as soil, gravel, or pavement | |
low_vegetation | 3 | Any generic type of vegetation like grass, bushes, shrubs, and trees | |
building | 6 | Man-made structures such as houses, offices, and industrial buildings | |
human_made_object | 64 | Any artificial objects not classified as buildings, such as vehicles, street furniture |
File > Save as...
and selecting the .laz
format. Select LAZ version 1.2 when exporting the file to .laz (not 1.3 or 1.4, which have issues with CloudCompare).You can contribute to this repository by adding new point clouds. They will be automatically evaluated and trained for you! To do so, you need to follow these steps:
datasets
folderAdd file -> Upload files
only) by dragging them to the upload area or by clicking on
choose your files`.Create a new branch
, then click on Commit changes
compare across forks
and select OpenDroneMap/ODMSemantic3D
repository as base and main
as base branch. Add a title and a description for the pull request and click on Create pull request
Github will run the training automatically and will post evaluation statistics in the pull request as a comment.
If the PR is accepted, the point cloud will be added to the repository and the new model will be published in a new release.
OpenDroneMap Contributors: ODMSemantic3D - An open photogrammetry dataset of classified 3D point clouds for automated semantic segmentation. https://github.com/OpenDroneMap/ODMSemantic3D