Into the Dark: Citizen Scientists Crowdsource for NASA, Can You Help?
NASA — the agency known for launching billion-dollar satellites and putting man on the moon — is hoping you can help them. Sadly, your mission won’t require you to don a spacesuit or to be strapped to a massive rocket. All you need is an internet connection, patience and comfortable place to sit.
As of August 2014 NASA has published more that 1.8 millions photos taken from space, most of which — 1.3 million — were taken from the International Space Station. The snaps date back to the early 1960s Mercury missions. So, why is NASA asking for your help?
Well, nearly 30 percent of the images were taken at night and NASA is hoping identifying cities on the images “could help save energy, contribute to better human health and safety and improve our understanding of atmospheric chemistry. But scientists need your help to make that happen.”
And images taken since 2012 are among the clearest nighttime pictures taken from the ISS. Prior to the installation of the European Space Angency’s “Night Pod,” night images from the ISS had a propensity to be blurry. That blurriness was due the space station’s speed — about 17,500-mph. The Night Pod is a motorized tripod that compensates for that mind-boggling speed. The result: Incredibly clear and detailed nighttime images.
While the images come from The Gateway to Astronaut Photography of Earth, a NASA website, the Complutense University of Madrid is leading the image cataloging. UCM has created the Cities at Night program to help citizen scientists sort through the massive collections.
This is also an example of the processing power of the mind adding more to the project than a computer. Alejandro Sanchez, a Ph.D. student at UCM explains, “In fact, without the help of citizens, it is almost impossible to use these images scientifically. Algorithms cannot distinguish between stars, cities, and other objects, such as the moon. Humans are much more efficient for complex image analysis.”
They’re cataloged into three components:
Dark Skies asks people to sort through pictures and discern which pictures are of stars, cities or objects. This level of the project is the easiest and requires little to no specific expertise.
This is where hyper-locality — from the citizen scientist’s familiarity of a geographic location — comes in handy. Participants are asked to identify specific points on a nighttime image and match them to corresponding locations on a map. The data gleaned from this part of the project will be used to create light maps of cities.
Lost at Night
Lost at Night requires the most amount of skill; participants are asked to points on nighttime images within a circle that has a 310-mile radius. Confounding this task: The direction of the camera isn’t known and according to Sanchez, “Some images are bright cities but others are small towns. It is like a puzzle with 300,000 pieces.”
Since the start of the project, people have responded in kind. And thus far, hundreds of volunteers have classified almost 20,000 images.
And to maintain the scientific veracity of the classifications, an image has to be classified by multiple individuals before it’s accepted.
Meteorologist Alan Raymond