Making space a valuable asset for everyone

KappaZeta is making Sentinel-1 data very easy to use
Copernicus Sentinel satellites produce several terabytes of data every day. For many users the data is complex to understand and use. KappaZeta is addressing this problem for Sentinel-1 with pre-processing and providing the derived layers in an analysis-ready format. The goal is to have a service, where obtaining the right satellite image takes either a single mouse click or an API command. The vision will be implemented under the InCubed programme of European Space Agency.
Our crop monitoring service prototype is live
In the beginning of May we launched our crop monitoring prototype service. It is the first phase of the Harvesting Time Recommendation service prototype and allows the farmers to see the ongoing season timeseries of Sentinel-1 and Sentinel-2 field-based features and NDVI/RGB/NRG images which are being updated as soon as new images are available.

Synthetic NDVI for proxy biomass calculations
Plant biomass, an indicator of the ecological state of an area, can be modeled from the Normalized Difference Vegetation Index (NDVI). The ESA’s Sentinel-2 satellite provides NDVI data openly, but this is often obstructed by clouds. Hence, we developed an MVP to explore the modeling of NDVI from Sentinel-1 data using Generative Models.
Our new cloudmask
On 23rd of April KappaZeta presented the Cloud Mask project results on Very High-resolution Radar & Optical Data Assessment (VH-RODA) 2021 workshop. We named our cloud classification mask KappaMask. Thus, we presented that our KappaMask outperformed Sen2Cor with 92% vs 57% dice coefficient on validation set. Check out our cloud mask comparison slider-pages with rule-based masks (Sen2Cor, Fmask) from here and machine learning-based Sinergise S2cloudless mask from here.

Advanced coherence rasters
It is a common understanding, that to reach a useful AI model, it is crucial to have validated ground reference data for training. Just as important is to have meaningful, calibrated, low noise features that have substantial physical relation to the phenomenon being modelled. At KappaZeta we are paying extra attention to get most out of Sentinel-1 SAR data. The figure below compares 6-day VH coherence from SNAP with the output from our processing chain. As you can see the spatial variability and the dynamic range from the KZ processor is much higher.
Geospatial Professionals in Tartu
Our founders Kaupo and Tanel talk in TartuGeo Podcast about their road to remote sensing, building KappaZeta and trends in Earth observation.

News archive >>
EU Subsidy Checks  
Replacing on-the-spot-checks on grasslands with an automated solution
Radar Time Series
Ready to use in various machine learning algorithms
Research and Consulting 
Developing new models and classifiers for different use cases