The code for the top three submissions were open sourced under the Apache 2 License on SpaceNet Github repository.ĬosmiQ Works conducted this project in coordination with the other SpaceNet Partners: Radiant Solutions, Amazon Web Services, and NVIDIA. The SpaceNet collaborators announced on April 24 that they are the recipients of the United States Geospatial Intelligence Foundation’s (USGIF) prestigious Industry.
The challenge was conducted from July 2017 to August 2017 and hosted on the TopCoder platform.
#Nvidia spacenet series#
TheSpaceNet partners also launched a series of public prize competitions to encourageimprovement of remote sensing machine learning algorithms. The new dataset contained over 300,000 manually curated building footprint features across the four cities. Accordingly, the SpaceNet partners(CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus oflabeled satellite imagery on Amazon Web Services (AWS) called SpaceNet. While it used the same evaluation metric from the previous challenge, SpaceNet 2 included an expanded dataset featuring four new cities, Paris, France, Shanghai, China, Khartoum, Sudan, and Las Vegas, Nevada, at a higher resolution than SpaceNet 1 - 30cm ground sample distance collected by DigitalGlobe’s Worldview 3 satellite. In building off of the results and lessons learned from SpaceNet 1, CosmiQ and the SpaceNet Partners decided to launch a second public data science challenge focused again on automated building footprint extraction from high resolution satellite imagery. The SpaceNet Dataset is hosted as an Amazon Web Services (AWS) Public Dataset.It contains 67,000 square km of very high-resolution imagery, >11M building footprints, and 20,000 km of road labels to ensure that there is adequate open source data available for geospatial machine learning research.
0 kommentar(er)
