Each SpaceNet challenge focuses on a different aspect of applying machine learning to solve difficult mapping challenges.
SpaceNet 7 Challenge: Multi-Temporal Urban Development
Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 232 United Nations Sustainable Development Goals, but the World Bank estimates that more than 100 countries currently lack effective Civil Registration systems. The SpaceNet 7 Multi-Temporal Urban Development Challenge aims to help address this deficit and develop novel computer vision methods for non-video time series data. In this challenge, participants identified and tracked buildings in satellite imagery time series collected over rapidly urbanizing areas. The competition centered around a new open source dataset of Planet satellite imagery mosaics, which included 24 images (one per month) covering ~100 unique geographies. The dataset comprised over 40,000 square kilometers of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 10 million individual annotations. Challenge participants were asked to track building construction over time, thereby directly assessing urbanization.