SpaceNet Challenge 6

Multi-Sensor All-Weather Mapping & Building Footprint Extraction

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SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping. SpaceNet is run in collaboration with CosmiQ Works, Maxar Technologies, Amazon Web Services (AWS), Intel AI, Capella Space, Topcoder, and IEEE GRSS.


Challenge Overview

Synthetic Aperture Radar (SAR) is a unique form of radar that can penetrate clouds, collect during all- weather conditions, and capture data day and night. Overhead collects from SAR satellites could be particularly valuable in the quest to aid disaster response in instances where weather and cloud cover can obstruct traditional electro-optical sensors. However, despite these advantages, there is limited open data available to researchers to explore the effectiveness of SAR for such applications, particularly at ultra-high resolutions.

This Challenge seeks to build upon the advances from SpaceNet 2 and 4 by challenging participants to automatically extract building footprints with computer vision and artificial intelligence (AI) algorithms using a combination of SAR and electro-optical imagery datasets. This openly-licensed dataset features a unique combination of half-meter SAR imagery from Capella Space and half-meter electro-optical (EO) imagery from Maxar’s WorldView 2 satellite. The area of interest for this challenge will be centered over the largest port in Europe: Rotterdam, the Netherlands. This area features thousands of buildings, vehicles, and boats of various sizes, to make an effective test bed for SAR and the fusion of these two types of data. This dataset can be found at the SpaceNet 6 Challenge overview page or its AWS S3 bucket.

In this challenge, the training dataset contains both SAR and EO imagery, however, the testing and scoring datasets contain only SAR data. Consequently, the EO data can be used for pre-processing the SAR data in some fashion, such as colorization, domain adaptation, or image translation, but cannot be used to directly map buildings. The dataset is structured to mimic real-world scenarios where historical EO data may be available, but concurrent EO collection with SAR is often not possible due to inconsistent orbits of the sensors, or cloud cover that will render the EO data unusable.

Evaluation Metric

In building off of the results from SpaceNet 1, 2, and 4, this will use the SpaceNet Metric for building footprint identification. This metric is an F1-score based on the intersection over union of two building footprints with a threshold of 0.5. Challenge participants will be allowed to leverage both the electro-optical and SAR datasets for model training. However, for scoring performance, only SAR data will be available to participants to ultimately map buildings.

Algorithmic Baseline

The algorithmic baseline for this Challenge will be made available in early March. An explanation of the baseline will be made available on CosmiQ Works’ blog, The DownLinQ.


The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to automatically extract information from satellite imagery at scale. Today, map features such as roads, building footprints, and points of interest are primarily created through manual techniques. SpaceNet believes that advancing automated feature extraction techniques will serve important downstream uses of map data, including humanitarian and disaster response. Furthermore, solving foundational mapping challenges are an important steppingstone to unleashing the power of advanced computer vision algorithms applied to a variety of remote sensing applications.

In Total Challenge Prizes


SpaceNet Challenge 6 Timeline

* Timeline subject to slight changes throughout course of challenge

Challenge Begins!


Midpoint Check-in!

Challenge Ends!

Winners Announced!





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