#Spacenet challenge code#

Furthermore, radar satellites do not require illumination and can capture data during both the day and the night.

Synthetic Aperture Radar sensors are unique as they can penetrate clouds and can collect during all weather conditions. As we look ahead to our next challenge, SpaceNet 6, we will push into a new frontier and an underexplored data modality: Synthetic Aperture Radar (SAR).įigure 1: A visualized return (HH polarization) from the SpaceNet 6 SAR dataset, displaying shipping and port activity at Eemhaven, near Pernis, Netherlands. Using these datasets, the SpaceNet public challenges have focused on several different geospatial aspects including: high resolution building footprint mapping, the effects of look angle on model performance, and creating routable road networks. km of imagery, 811,000 building footprints, and ~20,000 km of road labels across 10 cities. The SpaceNet team has open sourced ~27,000 sq. Spanning three years while featuring five unique datasets and challenges, SpaceNet has continued to focus on different aspects of applying machine learning to solve difficult foundational mapping problems. The competition will challenge developers to improve performance from the first competition using the higher-resolution imagery and more geographically diverse training data samples.EarthVision will feature the 6th SpaceNet The next phase of the SpaceNet Challenge will be a follow-on competition using DigitalGlobe’s 30 cm imagery from WorldView 3 and building footprints across new locations around the globe. The Rio geodatabase contains 12 datasets with 35 unique layers containing more than 120,000 individual points of interest.

National Geospatial-Intelligence Agency, which licensed the dataset produced by DigitalGlobe. This data is made available through the participation of the U.S.

#Spacenet challenge code#

“We are really thrilled by the developers’ level of engagement to use DigitalGlobe imagery, training data, and open-source code to create innovative algorithms,” said Tony Frazier, senior vice president and general manager of services at DigitalGlobe.Ī newly released Points of Interest (POI) dataset for Rio de Janeiro is now freely available to the public via SpaceNet on AWS. The winning algorithms will be made available to the open-source community through the SpaceNet GitHub repository and users of DigitalGlobe’s Geospatial Big Data platform (GBDX).

The participants submitted 242 solutions over a three-week period to compete for a total prize pool of $35,000 that was awarded to the top five performing contestants. SpaceNet is a collaboration between DigitalGlobe, CosmiQ Works and NVIDIA, which consists of an online repository of freely available satellite imagery, co-registered map layers to train algorithms, and public challenges that aim to accelerate innovation in machine learning.ĭigitalGlobe launched the first SpaceNet Challenge in November 2016, and 42 developers competed in an open challenge hosted by TopCoder to create algorithms that extract building footprints from satellite imagery. DigitalGlobe announced the results of the first SpaceNet Challenge, which will release openly licensed satellite imagery of Rio de Janeiro taken from the WorldView 2 satellite at 50cm Ground Sample Distance (GSD) using eight spectral bands.