Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

Understand

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 policy decisions and help direct humanitarian efforts.