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arXiv 1902.054332019-02cs.CVcs.LG

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

Swetava Ganguli, Jared Dunnmon, Darren Hau

备注：: Research performed as part of the Sustainability and Artificial Intelligence Laboratory (SAIL) at Stanford University. Second revised version corrects typographical errors and adds a few references

英文摘要：

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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