Abstract:
Obtaining detailed and reliable data about local economic livelihoods in developing
countries is expensive, and data are consequently scarce. 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. Our goal is to extract large-scale socioeconomic indicators from highresolution
satellite imagery.We therefore propose a transfer learning approach
where nighttime light intensities are used as a data-rich proxy. We train a fully
convolutional CNN model to predict nighttime lights from daytime imagery,
simultaneously learning features that are useful for poverty prediction. The
model learns filters identifying different terrains and man-made structures,
including roads, buildings, and farmlands, without any supervision beyond
nighttime lights.
