Artificial Cartographer
A tool for analyzing remote sensing data and aerial photographs using artificial intelligence models
artificial intelligence
remote sensing analytics
ABOUT PRODUCT
The platform combines 22 artificial intelligence models in one interface and supports satellite data from Sentinel-2, Landsat-8, Kanopus-V, Planet, Resurs-P systems. The results for segmentation are masks, the results for detection are shapefiles.
industry
agriculture
cartography
LOGGING
geodesy
ENERGETICS
Addresses issues
Enables the discovery and delineation of object boundaries within satellite images and more
Enhances the speed of obtaining image analysis results
Streamlines the process of developing and deploying AI models for remote sensing analysis
>50%
reduction in labor costs for developing and maintaining AI models for remote sensing analysis
reduction in time for integrating a finished model into the system functionality
>30%
Effects
PRODUCT BENEFITS
Proprietary software
Full pipeline and common outcome format
More than 20 object recognition classes
Library of ready-made AI models
CLIENTS AND PARTNERS
FAQ
Arable lands, capital construction projects, clouds, fields, deforestation, burnt surface of the earth, forest, ravines, hogweed, accumulations of garbage, disturbance of land cover, oil spills, overgrown agricultural lands, land plots, roads, quarries, swamps, crowns trees, water bodies, windfall.
Artificial Cartographer contains a set of ready-made templates that can be used to create models of other recognition classes. Templates save time and resources.
The platform supports working with satellite data from the Sentinel-2, Landsat-8, Kanopus-V, Planet and Resurs-P systems.
When performing segmentation of objects on satellite images, the platform provides masks that help highlight and limit the boundaries of objects. In the case of object detection, it provides shapefiles that contain information about the detected objects.
Yes, we provide technical support, regular updates and are ready to provide training for personnel.
Arable lands, capital construction projects, clouds, fields, deforestation, burnt surface of the earth, forest, ravines, hogweed, accumulations of garbage, disturbance of land cover, oil spills, overgrown agricultural lands, land plots, roads, quarries, swamps, crowns trees, water bodies, windfall.
Artificial Cartographer contains a set of ready-made templates that can be used to create models of other recognition classes. Templates save time and resources.
The platform supports working with satellite data from the Sentinel-2, Landsat-8, Kanopus-V, Planet and Resurs-P systems.
When performing segmentation of objects on satellite images, the platform provides masks that help highlight and limit the boundaries of objects. In the case of object detection, it provides shapefiles that contain information about the detected objects.
Yes, we provide technical support, regular updates and are ready to provide training for personnel.
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