“Radiant MLHub is an asset for data scientists and geospatial professionals globally,” says Radiant Earth Foundation Chief Data Scientist Dr.
The datasets and open source computer vision models will help disaster response workers and other researchers identify buildings quickly using automated techniques. To date, they have hosted five challenges, open sourced 23 winning algorithms, and distributed $250,000 in prizes. They use those datasets in public data science challenges focused on automated building footprint identification and road network extraction for routing. SpaceNet develops and open sources datasets of labeled, high-resolution satellite imagery over 10 urban areas including Shanghai, Khartoum, Mumbai, and Dar es Salaam. SpaceNet LLC is a nonprofit organization run in collaboration by IQT CosmiQ Works, Maxar Technologies, Amazon Web Services, Intel AI, Capella Space, Topcoder, and IEEE GRSS.
The “SpaceNet Roads and Building” dataset, on the other hand, focuses on the problem of object detection and classification in high-resolution imagery. This dataset helps improve generalization efforts and potentially can serve as a basis for similar watershed datasets. state, like Maryland, can be used to generalize land cover classification over the rest of the Chesapeake Bay region) - includes land cover classifications based on six classes, high-resolution USDA NAIP imagery, USGS Landsat 8 medium-resolution imagery and associated land cover classification, as well as Bing building masks. The “Chesapeake Bay Land Cover” dataset - which can be used to assess generalization of land cover classification methods (i.e., whether a model trained on data from one U.S. The new “Chesapeake Bay Land Cover” and “SpaceNet Roads and Buildings” training datasets are stored and managed by Microsoft AI for Earth and SpaceNet, respectively. Radiant MLHub is an interoperable solution for sharing training data and is compatible with all commercial and private cloud repositories. The addition to Radiant MLHub of the “Chesapeake Bay Land Cover” and “SpaceNet Roads and Buildings” training datasets will make it easier for individuals and organizations working on conservation, land cover and land use change, urban planning, rural development and related issues to discover and access data for use in training their machine learning algorithms and validating their models for accuracy. Moreover, Radiant MLHub features a global map of geospatial training data location that can be used to identify under-represented geographical areas from which more training data are needed. Shared data and models are accessible via a standardized API, and can therefore move across organizations, governments and sectors in order to unlock new opportunities for data-based insights. 11, 2019 (GLOBE NEWSWIRE) - Radiant Earth Foundation today announced the availability of Microsoft AI for Earth’s Chesapeake Bay Land Cover and SpaceNet’s Roads and Buildings training datasets through Radiant MLHub, an open digital training data repository that debuted earlier this week with “crop type” labels for major crops in Kenya, Tanzania and Uganda.ĭesigned to encourage widespread data collaboration, Radiant MLHub allows anyone to access, store, register and/or share open training datasets for high-quality Earth observations.
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