The advances in technology just keep getting better and better. At the present, machine learning is the one making all the noise because of the benefits that it can give to every aspect of human life. Machine Learning is one of the best innovations in the evolution of automation; and if it is built together with cloud computing, it can be a lot more beneficial.
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 'weakly labeled' lidar data, using 'unsupervised' pre-training with the cloud locations of the Wang & Sassen (2001) 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.