Accelerating Geospatial Machine Learning
SpaceNet delivers access to high-quality geospatial data for developers, researchers, and startups. Before SpaceNet, computer vision researchers had minimal options to obtain free, precision-labeled, and high-resolution satellite imagery. SpaceNet focuses on four open source key pillars: data, challenges, algorithms, and tools.
SpaceNet 7 Challenge – Launching Fall 2020
SpaceNet 7 Challenge: Multi-Temporal Urban Development
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 will identify and track buildings in satellite imagery time series collected over rapidly urbanizing areas. The competition centers around a new open source dataset of Planet satellite imagery mosaics, which will include 24 images (one per month) covering ~100 unique geographies. The dataset will comprise 40,000 km2 of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 3M individual annotations. Challenge participants will be asked to track building construction over time, thereby directly assessing urbanization.