AI-based Cloud Mask Processor for 


Develop new and better cloud mask than existing ones

Duration: 2020–2021
Funder: European Space Agency

Planned outcomes:

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

Grazing detection from Copernicus data for agricultural subsidy checks

A complete satellite-based grassland monitoring service

Duration: 2020–2021
Funder: European Space Agency
Partners: Gisat s.r.o

Planned outcomes:

  • Develop grazing detection methodology based on Copernicus data (Sentinel-1 and Sentinel-2 imagery time series).
  • Provide the NPA operators means to carry out checks on grasslands using EO data and substituting on the spot checks of grassland grazing activity with new EO data based grazing detection methodology.
  • Close the grasslands subsidy checks case for CAP satellite monitoring.
Grazing detection project website

Harvesting Time Recommendation for maximum crop Yield (HaTRY)

Predicting the best time to harvest crop using remotely sensed data

Duration: 2020–2021
Funder: European Space Agency
Partners: local farmers in Estonia

Planned outcomes:

  • Prototype service for Precision Farming application to predict and recommend the most favorable harvesting time for maximum crop yield.
  • Focus on Northern Europe region and three most common crops: winter wheat, spring barley and winter rapeseed.
  • Learn and collect farmers requirements for a fully operational service.

National Programme for Addressing Socio-Economic Challenges through R&D (RITA). Using remote sensing data in favour of the public sector services

Monitoring the use of agricultural land

Duration: 2019–2020
Funder: This study was financially supported by the European Regional Development Fund within National Programme for Addressing Socio-Economic Challenges through R&D (RITA).
Partners: Consortium team: University of Tartu, Tallinn University of Technology, Estonian University of Life Sciences, KappaZeta Ltd

Planned outcomes:

  • Mature, reliable and tested crop classification methodology development specifically suited for Estonian agricultural, ecological and climatic conditions.
  • Multi-year country-wide testing and error analysis about vegetative seasons 2018–2020.
  • Crop classification model prototype with test datasets.


ESA Business Incubation

Improving our software quality during the incubation phase

Duration: 2018–2019 

Funder: European Space Agency
Partners: University of Tartu (Institute of Computer Science), Sookolli Kaardid OÜ, Elmer SKB OÜ, Codeborne OÜ

  • Satellite imagery model development
  • Software architecture definition and review
  • Software implementation
  • Market analysis and business plan development
  • Improved web map for visualizing our analysis results

Detection of mowing events on grasslands from Sentinel-data

The first nation-wide system for automated monitoring of agricultural practices in EU 

Duration: 2016-2018
Customer: Estonian Agricultural Registers and Information Board (ARIB)
Partners: CGI Estonia, Tartu Observatory

  • Nation-wide fully automated mowing detection system operational in Estonia from 2018
  • Sentinel-1 and Sentinel-2 time-series for operational near real-time monitoring
  • 85% of detection accuracy of the mowing events on grasslands
  • Automated “early warning” reminders to applicants

 An isolated grassland – status “not mowed”

Grassland mowing detection for agricultural subsidy checks with Sentinel-1 and Sentinel-2

Looking over the borders to Denmark, Sweden and Poland 

Duration: 2017-2019.
Customer: European Space Agency, Industry Incentive Scheme (ESA IIS)
Partners: The Danish Agrifish Agency, the Swedish Board of Agriculture, The Agency for Restructuring and Modernisation of Agriculture in Poland, Reach-U Ltd, KappaZeta Ltd

  • Validate the service by performing user trials in Sweden and Denmark
  • Enhance the existing cutting and grazing detection methodology
  • Study Earth Observation Community Platform (EO CP) service providers
  • Perform Viability Analysis

Data Analytics for Optimizing Agricultural Monitoring

Going beyond the mowing detection powered by high-level data analytics expertise 

Duration: 2017-2018.
Funder: Enterprise Estonia, project No. EU48684
Partners: Software Technology and Applications Competence Centre (STACC), KappaZeta

  • Develop a scientifically validated methodology for ploughing and grazing events detection from Sentinel-1 and Sentinel-2 time series
  • Bring to customers a new cultivation and grazing detection product prototype
  • Involve high-level data analytics expertise powered by STACC.

Home page and demo application

KZ branding, home page and demo application

Duration: 2017
Funder: Enterprise Estonia, project No. EU51738; amount of the grant 14,960 €.
Main partners: Reach-U, Kiften, Jon & Pun, KappaZeta
Outcomes: KZ branding, home page and mowing detection demo application