  Road networks are among the most essential components of a country's
infrastructure. By facilitating the movement and exchange of goods, people, and
ideas, they support economic and cultural activity both within and across
borders. Up-to-date mapping of the the geographical distribution of roads and
their quality is essential in high-impact applications ranging from land use
planning to wilderness conservation. Mapping presents a particularly pressing
challenge in developing countries, where documentation is poor and
disproportionate amounts of road construction are expected to occur in the
coming decades. We present a new crowd-sourced approach capable of assessing
road quality and identify key challenges and opportunities in the
transferability of deep learning based methods across domains.
