Menu

SpaceNet

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 9: Cross-Modal Satellite Imagery Registration

Swift and effective disaster response often relies on the integration and analysis of diverse remote sensing data sources such as electro-optical and Synthetic Aperture Radar (SAR). However, the co-registration of optical and SAR imagery remains a major challenge due to the inherent differences in their acquisition methods and data characteristics. SpaceNet 9 aims to address this issue by focusing on cross-modal image registration, a critical preprocessing step for disaster analysis and recovery. Participants in this challenge will develop algorithms to compute pixel-wise spatial transformations between optical imagery and SAR imagery, specifically in earthquake-affected regions. These algorithms will be evaluated for their ability to align tie-points across modalities, enabling better downstream analytics such as damage assessment and change detection.

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.

Collaborators

AWS-Logo_Full-Color_1000x600

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

NGA
2016 – 2020

Planet
2019 – 2020