Custom Projects

The EOS Platform that stands behind all of our end-user products is capable of processing large amounts of data, performing tasks on preprocessed satellite and other types of data, as well as performing analytics on both internal and external models. This allows us to deliver diverse, large-scale custom projects such as:

Ukraine
World Bank, within the framework of large-scale projects
“Supporting Transparent Land Governance in Ukraine”
Features
  • Crop type classifications (7 types)
  • Crop classification maps (2016, 2017, 2018 and 2019)
  • Identification of a total area for each type of crops
  • Land coverage classifications (forests, water, croplands, artificial land cover)
  • Crop rotation (detecting crop rotation violations in order to prevent tax evasion)
  • Neural network training; uploading ground data to the system
  • Adding the results to the confusion matrix.
All results were officially validated by the World Bank Commission
Wheat
Barley
Tabacco
Forest
Cotton
Azerbaijan
Within the frameworks of a nationwide “Development Program” aimed at restoring the cotton market
Features
  • Land coverage classification (forests, water, croplands, artificial land cover)
  • Obtaining crop statistical data (wheat, barley, cotton, tobacco)
  • Crop classification mapping for 2018 (wheat, barley, cotton, tobacco)
  • Crop yield forecasting for the current season, for 64 districts
Kazakhstan
Features
  • Crop type classification (forests, water, croplands, artificial land cover)
  • Cereal crops map development for 2018
  • Crop yield forecasting on the district level
  • Crop conditions map for croplands
  • Harvest dynamics monitoring
United Kingdom
Potatoes Maincrop
Winter Wheat
Cauliflowers
Winter Rapessed
Sugar Beet
Maize
Spring Barley
Cauliflowers
Beans Dried Spring
Features
  • Land coverage classification (forests, water, croplands, artificial land cover)
  • Field contour identification using our Neural Network
  • Crop type classification (9 types)
  • Crop classification mapping for 2016
  • Total area for each crop type identification
  • Crop class contours identification
  • Crop conditions monitoring
United States
Features
  • Crop map development for 2018 (winter wheat, corn, cotton, maize, soybeans)
  • Crop conditions monitoring
  • Winter Wheat
  • Corn
  • Cotton
  • Maize
  • Soybean
Argentina, Brazil
Features
  • Remote crop classification, based on ground data (wheat, maize, soybean, cotton)
  • Soil type classification (Mato Grosso)
  • Soil moisture mapping
  • Expansion and validation of the algorithms

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