![]() ![]() In this paper we discuss the SpaceNet imagery, labels, evaluation metrics, prize challenge results to date, and future plans for the SpaceNet challenge series. The first two of these competitions focused on automated building footprint extraction, and the most recent challenge focused on road network extraction. The SpaceNet partners also launched a series of public prize competitions to encourage improvement of remote sensing machine learning algorithms. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Services (AWS) called SpaceNet. We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques. The second round included more data, new imagery formats, and improved building footprint annotations. II As with SpaceNet 1, this challenge tasked competitors with developing automated methods for extracting map-ready building footprints from high-resolution satellite imagery. Any winning open-source algorithm from SpaceNet 1-7 may also be used. SpaceNet 2: Building Extraction Challenge Pt. Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. During SpaceNet 8, challenge participants will train algorithms on imagery and labels from previous challengesas well as newly created labeled training datasets from Maxarto rapidly map an area affected by flooding. Foundational mapping remains a challenge in many parts of the world, particularly in dynamic scenarios such as natural disasters when timely updates are critical. ![]()
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