An accurate AI-based Cloud Mask Processor for Sentinel-2
Industrial organisations involved
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.
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.
Solution
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.
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.
Benefits
- 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.