ML Anthology

LiDAR Cloud Detection with Fully Convolutional Networks

Erol Cromwell, Donna Flynn
WACV 2019 pp. 619-627
doi:10.1109/WACV.2019.00071 /wacv/2019/cromwell2019wacv-lidar/

Abstract

In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time-height cloud locations with a fully convolutional network (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with image-level annotations, pre-training the entire FCN with the cloud locations of the MPLCMASK cloud mask algorithm, and fully supervised learning with hand-labeled cloud locations. We show the model achieves higher levels of cloud identification compared to the cloud mask algorithm implementation.

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Cite

Text

Cromwell and Flynn. "LiDAR Cloud Detection with Fully Convolutional Networks." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019. doi:10.1109/WACV.2019.00071

Markdown

[Cromwell and Flynn. "LiDAR Cloud Detection with Fully Convolutional Networks." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019.](https://mlanthology.org/wacv/2019/cromwell2019wacv-lidar/) doi:10.1109/WACV.2019.00071

BibTeX

@inproceedings{cromwell2019wacv-lidar,
  title     = {{LiDAR Cloud Detection with Fully Convolutional Networks}},
  author    = {Cromwell, Erol and Flynn, Donna},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2019},
  pages     = {619-627},
  doi       = {10.1109/WACV.2019.00071},
  url       = {https://mlanthology.org/wacv/2019/cromwell2019wacv-lidar/}
}