  Foundational mapping remains a challenge in many parts of the world,
particularly in dynamic scenarios such as natural disasters when timely updates
are critical. Updating maps is currently a highly manual process requiring a
large number of human labelers to either create features or rigorously validate
automated outputs. We propose that the frequent revisits of earth imaging
satellite constellations may accelerate existing efforts to quickly update
foundational maps when combined with advanced machine learning techniques.
Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and
NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web
Services (AWS) called SpaceNet. The SpaceNet partners also launched a series of
public prize competitions to encourage improvement of remote sensing machine
learning algorithms. The first two of these competitions focused on automated
building footprint extraction, and the most recent challenge focused on road
network extraction. In this paper we discuss the SpaceNet imagery, labels,
evaluation metrics, prize challenge results to date, and future plans for the
SpaceNet challenge series.
