Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

  title={Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping},
  author={Sang Michael Xie and Neal Jean and Marshall Burke and David B. Lobell and Stefano Ermon},
The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform… CONTINUE READING
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Transfer learning from deep features for remote sensing and poverty mapping

  • M. Xie, N. Jean, M. Burke, D. Lobell, S. Ermon
  • CoRR abs/1510.00098.
  • 2015
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A world that counts: Mobilising the data revolution for sustainable development

  • Independent Expert Advisory Group Secretariat.
  • Technical report.
  • 2014

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