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ODMSemantic3D - An open photogrammetry dataset of classified 3D point clouds for automated semantic segmentation

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.

Contribute a point cloud

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:

If you are starting from an unclassified point cloud you can initialize the classification values by going to Edit > Add scalar field > Classification

add-scalar-field

At a minimum, the point cloud should have the following classification codes:

ClassNumberDescription
ground2Earth's surface such as soil, gravel, or pavement
low_vegetation3Any generic type of vegetation like grass, bushes, shrubs, and trees
building6Man-made structures such as houses, offices, and industrial buildings
human_made_object64Any artificial objects not classified as buildings, such as vehicles, street furniture

classify-proc

Open a pull request

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:

create-fork

create-fork-next

click-on-datasets-folder

upload-files

commit-changes

create-pull-request

Citation

OpenDroneMap Contributors: ODMSemantic3D - An open photogrammetry dataset of classified 3D point clouds for automated semantic segmentation. https://github.com/OpenDroneMap/ODMSemantic3D