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:

United Kingdom
schematic field zoning
Potatoes Maincrop icon
Potatoes Maincrop
Winter Wheat icon
Winter Wheat
Cauliflowers icon
Cauliflowers
Winter Rapessed icon
Winter Rapessed
Sugar Beet icon
Sugar Beet
Maize icon
Maize
Spring Barley icon
Spring Barley
Cauliflowers icon
Cauliflowers
Beans Dried Spring icon
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
crop development map and graph
  • Winter Wheat
  • Corn
  • Cotton
  • Maize
  • Soybean
Crop Monitoring
Monitor the state of your crops right from the office, learn about the slightest changes on-the-spot, make fast and reliable decisions on field treatment
Crop Monitoring software in laptop
Argentina, Brazil
colorful scheme of zoned fields
schematic graph
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
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.
map of Ukraine with crop dots on it
example chart
map of Azerbaijan with crop dots on it
Wheat
Wheat
Barley
Barley
Tabacco
Tabacco
Forest
Forest
Cotton
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
cereal crops map development for 2018 scheme

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