PyTorch Pointnet++ Segmetation on DALES dataset

This project aims to leverage PointNet++, a successor of PointNet to perform semantic segmentation on point cloud obtain form Airborne Laser Scanner (ALS) such as DALES dataset using Pytorch and Pytorch Geometrics.

The pretrained model is trained with 39 epoches on the total of 60 millions points using Adam optimizer with fixed learning rate of 0.001. Then, the model is evaluated on the test dataset which consist of around 22 millions points.

Tile size	Metrics	Ground	Vegetation	Car	Truck	Powerline	Fence	Poles	Building	OA
15x15	Precision	0.93	0.93	0.39	0.18	0	0	0	0.81	0.9
	Recall	0.95	0.87	0.08	0.2	0	0	0	0.88
	IOU	0.94	0.9	0.132	0.103	0	0	0	0.73
25x25	Precision	0.91	0.938	0.18	0.1	0	0	0	0.49	0.85
	Recall	0.87	0.87	0.2	0.2	0	0	0	0.6
	IOU	0.8	0.82	0.103	0.1	0	0	0	0.43

Dataset

DALES dataset was collected by University of Daytons using a Riegl Q1560 dualchannel. The entire aerial LiDAR collection spanned $330 km^2$ over the City of Surrey in British Columbia, Canada. However, only $10 km^2$ of data has labels.

The dataset consist of 505 millions points which places it at the biggest dataset for ALS at that time.

There are 8 categories, labelled as follow:

• 0: Unknowns
• 1: Ground
• 2: Vegetation
• 3: Cars
• 4: Truck
• 5: Power line
• 6: Fences
• 7: Poles
• 8: Building

For more information about DALES dataset, please visit this site

Setup

Usage

Roadmap

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Author

Harvey Pham @Linkedin Email: qhuy.phm@gmail.com

Acknowledgements

Thank you