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

Swetava Ganguli; Jared Dunnmon; Darren Hau

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

Computer Vision and Pattern Recognition 2019-04-29 v2 Machine Learning

Authors: Swetava Ganguli , Jared Dunnmon , Darren Hau

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.

Keywords

convolutional neural network remote sensing food computing

Cite

@article{arxiv.1902.05433,
  title  = {Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking},
  author = {Swetava Ganguli and Jared Dunnmon and Darren Hau},
  journal= {arXiv preprint arXiv:1902.05433},
  year   = {2019}
}

Comments

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

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

Abstract

Keywords

Cite

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