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README.md
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## Uses
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## Bias, Risks, Limitations and Recommendations
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## Uses
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The model was specifically trained and designed for the segmentation of aerial lidar point clouds from the Lidar HD program (2020-2025),
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an ambitious initiative that aim to obtain a 3D description of the French territory by 2026.
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While the model could be applied to other types of point clouds, Lidar HD data have specific geometric specifications. Furthermore, the training data was colorized
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with very-high-definition aerial images from the ([BD ORTHO®](https://geoservices.ign.fr/bdortho)), which have their own spatial and radiometric specifications.
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Consequently, the model's prediction would improve for aerial lidar point clouds with similar densities and colorimetries than the original ones.
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**Data preprocessing**
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Point clouds were preprocessed for training with point subsampling, filtering of artefacts points, on-the-fly creation of colorimetric features, and normalization of features and coordinates.
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For inference, the same preprocessing should be used (refer to the inference configuration and to the code repository).
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**Inference library: Myria3D**
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Model was trained in an open source deep learning code reposiroty developped in-house, and inference is only supported in this library.
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The library comes with a Dockerfile as well as detailed documentation for inference.
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Patched inference from large point clouds (e.g. 1 x 1 km Lidar HD tiles) is supported, with or without (default) overlapping sliding windows.
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The original point cloud is augmented with several dimensions: a PredictedClassification dimension, an entropy dimension, and (optionnaly) class probability dimensions (e.g. building, ground...).
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Refer to Myria3D's documentation for custom settings.
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## Bias, Risks, Limitations and Recommendations
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