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Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
[article]
2016
arXiv
pre-print
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
arXiv:1510.00098v2
fatcat:7vj33rshxjdatidqwcouasvt2m