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: 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.

Celebrating SpaceNet

For the past five years SpaceNet has been accelerating open source, applied research in geospatial machine learning. Nine supporting Partner organizations and seven challenges later, this video highlights the key accomplishments thus far of this one-of-a-kind initiative.

SpaceNet Challenge Datasets

SpaceNet Hosted Datasets

Algorithm Repository

Explore algorithms used to solve SpaceNet challenges.  These currently include 13 open source Building footprint and Road Network extraction problems.



Alumni Collaborators

IQT Labs’ CosmiQ Works
Co-founder & Managing Partner​
Challenge Manager: SpaceNets 1-7
2016 – 2021

Capella Space
2019 – 2021

Intel AI
2018 – 2020

2016 – 2020

2019 – 2020