  Segmenting clouds in high-resolution satellite images is an arduous and
challenging task due to the many types of geographies and clouds a satellite
can capture. Therefore, it needs to be automated and optimized, specially for
those who regularly process great amounts of satellite images, such as
governmental institutions. In that sense, the contribution of this work is
twofold: We present the CloudPeru2 dataset, consisting of 22,400 images of
512x512 pixels and their respective hand-drawn cloud masks, as well as the
proposal of an end-to-end segmentation method for clouds using a Convolutional
Neural Network (CNN) based on the Deeplab v3+ architecture. The results over
the test set achieved an accuracy of 96.62%, precision of 96.46%, specificity
of 98.53%, and sensitivity of 96.72% which is superior to the compared methods.
