Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking

Swetava Ganguli; Jared Dunnmon; Darren Hau

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arXiv · 1902.05433

Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking

Swetava Ganguli·Jared Dunnmon·Darren Hau

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Abstract

Obtaining reliable data describing local Food Security Metrics (FSM) at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world. We train a CNN on publicly available satellite data describing land cover classification and use both transfer learning and direct training to build a model for FSM prediction purely from satellite imagery data. We then propose efficient tasking algorithms for high resolution satellite assets via transfer learning, Markovian search algorithms, and Bayesian networks.

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BibTeX RIS

Swetava Ganguli, Jared Dunnmon, Darren Hau. 2019-02-13. Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking. https://arxiv.org/abs/1902.05433

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