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
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 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 includes 24 images (one per month) covering ~100 unique geographies. The dataset will comprise 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 will be asked to track building construction over time, thereby directly assessing urbanization.
SpaceNet Celebrates Four Years!
As we celebrate four years of accelerating open source, applied research in geospatial machine learning, we reflect on lessons learned, our strategic focus, and next steps. Thank you to all of our Partners for making this possible.
A Collaboration Between SpaceNet & GroundWork
Algorithm Repository
Explore algorithms used to solve SpaceNet challenges. These currently include 13 open source Building footprint and Road Network extraction problems.
Collaborators
