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--- |
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license: etalab-2.0 |
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tags: |
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- pytorch |
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- segmentation |
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- point clouds |
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- aerial lidar scanning |
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- IGN |
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model-index: |
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- name: FRACTAL-LidarHD_7cl_randlanet |
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results: |
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- task: |
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type: semantic-segmentation |
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dataset: |
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name: IGNF/FRACTAL |
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type: point-cloud-segmentation-dataset |
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metrics: |
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- name: mIoU |
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type: mIoU |
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value: 77.2 |
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- name: IoU Other |
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type: IoU |
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value: 48.1 |
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- name: IoU Ground |
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type: IoU |
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value: 91.7 |
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- name: IoU Vegetation |
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type: IoU |
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value: 93.7 |
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- name: IoU Building |
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type: IoU |
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value: 90.0 |
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- name: IoU Water |
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type: IoU |
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value: 90.8 |
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- name: IoU Bridge |
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type: IoU |
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value: 63.5 |
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- name: IoU Permanent Structure |
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type: IoU |
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value: 59.9 |
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--- |
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<div style="border:1px solid black; padding:25px; background-color:#FDFFF4 ; padding-top:10px; padding-bottom:1px;"> |
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<h1>FRACTAL-LidarHD_7cl_randlanet</h1> |
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<p>The general characteristics of this specific model <strong>FRACTAL-LidarHD_7cl_randlanet</strong> are :</p> |
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<ul style="list-style-type:disc;"> |
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<li>Trained with the FRACTAL dataset</li> |
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<li>Aerial lidar point clouds, colorized with rgb + near-infrared, with high point density (~40 pts/m²)</li> |
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<li>RandLa-Net architecture as implemented in the Myria3D library</li> |
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<li>7 class nomenclature : [other, ground, vegetation, building, water, bridge, permanent structure]</li> |
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</ul> |
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</div> |
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## Model Informations |
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- **Code repository:** https://github.com/IGNF/myria3d (V3.8) |
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- **Paper:** TBD |
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- **Developed by:** IGN |
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- **Compute infrastructure:** |
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- software: python, pytorch-lightning |
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- hardware: in-house HPC/AI resources |
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- **License:** : Etalab 2.0 |
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--- |
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## Uses |
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The model was specifically trained and designed for the **semantic segmentation of aerial lidar point clouds from the Lidar HD program (2020-2025)**. |
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The Lidar HD is 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](https://geoservices.ign.fr/lidarhd) 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. 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_**: 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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**_Multi-domain model_**: The FRACTAL dataset used for training covers 5 spatial domains from 5 southern regions of metropolitan France. |
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The 250 km² of data in FRACTAL were sampled from an original 17440 km² area, and cover a wide diversity of landscapes and scenes. |
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While large and diverse, this data only covers a fraction of the French territory, and the model should be used with adequate verifications when applied to new domains. |
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This being said, while domain shifts are frequent for aerial imageries due to different acquisition conditions and downstream data processing, |
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the aerial lidar point clouds are expected to have more consistent characteristiques |
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(density, range of acquisition angle, etc.) across spatial domains. |
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## Bias, Risks, Limitations and Recommendations |
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--- |
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## How to Get Started with the Model |
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Model was trained in an open source deep learning code repository developped in-house: [github.com/IGNF/myria3d](https://github.com/IGNF/myria3d)). |
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Inference is only supported in this library, and inference instructions are detailed in the code repository documentation. |
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Patched inference from large point clouds (e.g. 1 x 1 km Lidar HD tiles) is supported, with or without (by 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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For convenience and scalable model deployment, Myria3D comes with a Dockerfile. |
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--- |
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## Training Details |
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### Training Data |
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### Training Procedure |
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#### Preprocessing |
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#### Training Hyperparameters |
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#### Speeds, Sizes, Times |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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#### Metrics |
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### Results |
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Samples of results |
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--- |
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## Citation |
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**BibTeX:** |
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``` |
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``` |
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**APA:** |
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``` |
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``` |
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## Contact : TBD |
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