  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.
