Our collaborations strive to continuously advance the science and innovative AI methodologies. Together with our partners, we are building tools and data to support more resilient, sustainable, and equitable economies.
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Working to identify major technical opportunities and challenges in the advancement of crop analytics for smallholder farmers.
Creating sub-national maize yield estimates following the 2016-2019 long rain harvest seasons for the Ministry of Agriculture.
Developing and testing machine learning approaches to predict poverty rates and welfare related indicators.
Assessing the tradeoffs between the spatial anonymization of survey data and the accuracy of high-resolution poverty predictions.
Integrating satellites and surveys for high resolution crop type mapping and crop yield estimation in resource constrained settings.
SBIR Phase I and Phase II awards enabling technical research toward the development of yield forecasting models based on satellite observations.