Success Story: An accurate AI-based Cloud Mask Processor for Sentinel-2

NCC presenting the success story

The NCC Estonia coordinates HPC expertise at national level. Our mission is to analyse, implement and coordinate all necessary activities and offer services to end users to cover their needs: from access to resources, from technological consultancy to the provision of training courses for academia, public administrations and industry.

Technical/scientific Challenge:

Cloud masking is an essential step for the pre-processing of optical satellite imagery. KappaZeta addresses the problem by introducing KappaMask, an AI-based cloud and cloud shadow masking processor for Sentinel-2, which carries an optical instrument payload that samples 13 spectral bands. As a cloud detector, KappaMask uses a large convolutional segmentation model. Faster model convergence during training can be achieved by using larger batch sizes of the training data, which means more GPU memory is needed. Additionally, faster CPUs are required for shorter data loading times to increase the training speed even further.

Business impact:

KappaMask is an open source project. All the results, final software and source code will be freely and openly distributed in GitHub. Openness and accessibility of the software should directly translate into greater usage.

Partners involved in the success story:

KappaZeta is a science-driven remote sensing company aiming to make space a valuable asset for everyone. KappaZeta’s expertise is in using SAR (radar) satellite data, incorporating it with optical satellite data and providing some of the most accurate AI models on the market. The key area of focus is agriculture.


KappaMask was trained on an open-source dataset and fine-tuned on a Northern European terrestrial dataset which was labelled manually using the active learning methodology. The training was performed on the University of Tartu’s HPC Centres’ high-performance compute nodes. Powerful GPUs and CPUs were applied to substantially speed up the training of the model.


The main outcomes of the project are as follows:

  • Reliable cloud mask processor for Northern Europe region, which is compatible with ESA Sentinel-2 L2 processing chain.
  • Creation of high quality reference dataset for future developments.
  • Innovative application of deep learning techniques in cloud masking.


  • Keywords: remote sensing, machine learning, image segmentation, cloud mask, convolutional neural network
  • Industry sector: Agriculture
  • Technology: HPC, HPDA, AI, deep learning


Ülar Allas,

This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 951732. The JU receives support from the European Union’s Horizon 2020 research and innovation program and Germany, Bulgaria, Austria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, the United Kingdom, France, the Netherlands, Belgium, Luxembourg, Slovakia, Norway, Switzerland, Turkey, Republic of North Macedonia, Iceland, Montenegro