  Building footprints provide a useful proxy for a great many humanitarian
applications. For example, building footprints are useful for high fidelity
population estimates, and quantifying population statistics is fundamental to
~1/4 of the United Nations Sustainable Development Goals Indicators. In this
paper we (the SpaceNet Partners) discuss efforts to develop techniques for
precise building footprint localization, tracking, and change detection via the
SpaceNet Multi-Temporal Urban Development Challenge (also known as SpaceNet 7).
In this NeurIPS 2020 competition, participants were asked identify and track
buildings in satellite imagery time series collected over rapidly urbanizing
areas. The competition centered around a brand new open source dataset of
Planet Labs satellite imagery mosaics at 4m resolution, which includes 24
images (one per month) covering ~100 unique geographies. Tracking individual
buildings at this resolution is quite challenging, yet the winning participants
demonstrated impressive performance with the newly developed SpaceNet Change
and Object Tracking (SCOT) metric. This paper details the top-5 winning
approaches, as well as analysis of results that yielded a handful of
interesting anecdotes such as decreasing performance with latitude.
