Note: SpaceNet is a collaborative effort between CosmiQ Works, DigitalGlobe and Radiant Solutions hosted on Amazon Web Services as a public dataset. (To learn more visit https://spacenetchallenge.github.io/ ).
SpaceNet is proud to release the Off-Nadir Imagery dataset. This dataset was specifically built to explore advanced algorithms capabilities to process high off-nadir imagery. The dataset includes 27 WorldView 2 Satellite images from 7 degrees to 54 degrees off-nadir all captured within 5 minutes of each other. The dataset covers over 665 square kilometers of downtown Atlanta and ~126,747 buildings footprints labeled from a nadir image. …
In March, we concluded the SpaceNet Road Detection and Routing Challenge hosted by CosmiQ Works, Radiant Solution and NVIDIA. Accurate road networks are an important map feature that is required for everything from logistics planning to turn-by-turn directions. Currently, road networks are traced by hand from overhead imagery or created from ground surveys. The SpaceNet Road Detection and Routing Challenge tasked competitors to develop algorithms to extract road networks from satellite imagery.
For the competition, SpaceNet released a new roads dataset with over 8000 kilometers of roads labeled, and introduced a novel metric for road extraction, Average Path Length Similarity…
Note: SpaceNet is a collaborative effort between CosmiQ Works, Radiant Solutions and NVIDIA. For more information see the SpaceNet Road Detection and Routing Challenge Announcement.
Millions of kilometers of the worlds’ roadways remain unmapped. In fact, there are large organizations such as the Humanitarian OpenStreetMap Team Missing Maps Project whose entire goal is to map missing areas. Since 2014, over 41,000 contributors have labeled 16 million buildings and manually traced and digitized 13 million km of roadways. Machine learning could play a key role in accelerating this process.
The SpaceNet Road Detection and Routing Challenge is designed to assist the…
In June, we concluded the second SpaceNet competition, a satellite imagery object detection competition hosted by CosmiQ Works, DigitalGlobe, and NVIDIA. This competition challenged contestants to submit algorithms that automatically detect building footprints in satellite images, continuing the challenge originally posed in the first SpaceNet competition.
As explained in a prior post, our second competition used a new dataset consisting of satellite imagery from WorldView-3 and labeled building footprints over four cities: Las Vegas, Paris, Shanghai and Khartoum. Details about the dataset can be found in that post, including how it differs from that used in the first competition.
David Lindenbaum
Principal Engineer @ CosmiQ Works
