%X Building footprints provide a useful proxy for a great many humanitarian applications. %C Proceedings of Machine Learning Research %B Proceedings of the NeurIPS 2020 Competition and Demonstration Track %T The SpaceNet Multi-Temporal Urban Development Challenge 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.Ĭite this = 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. 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.
SPACENET SATELLITE SERIES
In this NeurIPS 2020 competition, participants were asked identify and track buildings in satellite imagery time series collected over rapidly urbanizing areas. 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). 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. Building footprints provide a useful proxy for a great many humanitarian applications.
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