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Radiant Earth Foundation

Spot the Crop Data Challenge focuses on using satellite imagery and machine learning for automating South African crop identification

13 Jul - 15:14 SAST
Radiant Earth Foundation’s Spot the Crop Data Challenge seeks entrants to predict crop types in the Western Cape, South Africa using satellite image time-series.
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Radiant Earth partners with Microsoft to accelerate earth observation solutions for local and global sustainability applications

13 Jul - 14:59 SAST
(AI) data, tools, and educational resources to address sustainability challenges.
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Radiant Earth Foundation releases its STAC API 1.0

11 Dec - 20:22 SAST
Radiant Earth Foundation has released its first Spatio Temporal Asset Catalogue (STAC) API as version 1.0.0-beta
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African contestants take top prizes at global satellite data labelling contest

25 Sep - 20:52 SAST
Participants were asked to identify cloudy pixels in Sentinel-2 scenes, to help develop a large-scale accurate cloud detection training dataset.
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Radiant Earth Foundation leadership transition

26 Aug - 18:10 SAST
Anne Hale Miglarese hands over the executive directorship to chief data scientist Hamed Alemohammad, and Jerry Johnston becomes the chairman of the board of directors.
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Radiant Earth Foundation releases benchmark land cover training data for Africa

28 Jul - 15:14 SAST
Radiant Earth Foundation has released “LandCoverNet,” a human-labelled global land cover classification training dataset. This release contains data across Africa, which accounts for ~1/5 of the global dataset.
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Crop detection in Africa competition winners announced

16 Apr - 23:33 SAST
Five data scientists emerged as winners of Radiant Earth Foundation’s competition, in partnership with Zindi Africa, to create a machine learning model that classifies farm fields in Kenya by crop type using time series...
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Open geospatial training data released for crop type machine learning

9 Dec - 19:04 SAST
Radiant MLHub debuted its cloud-based open library of earth observation training data with “crop type” training data for major crops in Kenya, Tanzania and Uganda.
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