Tuesday, January 17, 2017
This December, I had the opportunity to present a poster at the American Geophysical Union (AGU) in San Francisco. With over 24,000 people attending, the meeting was spread across four enormous buildings called the Moscone Center.
Half of one of the buildings making up the Moscone Center. Apologies for the blurry photo
On the first day, the Bright Stars Program - an initiative supporting high school research - hosted a field trip to various scenes around San Francisco. We started by driving down to a national park, where we learned about whaling and hiked a trail or two before heading back towards the city. Next we toured a facility doing ocean research with car-sized ROVs. The tour included a visit to their manufacturing space, where they were in the process of building autonomous torpedos to map the seafloor. After the tour, our guide talked about some of the discoveries their ROVs had made. He explained that there was a backlog of species waiting to be officially “discovered” because they didn’t have the time to do all of the paperwork.
That night, I presented my poster at a “practice session” of sorts. I talked to around 40 people over the next few hours; many talked about the interesting research they were involved in. One woman had spent the last three years working on a very similar problem as the one my research addressed, with a very different approach.
I suppose I should explain my project. Over the last few months I have been attempting to create cloud masks for satellite imagery. NASA has launched several satellites under the LandSat program to image the earth and researchers use these images to watch the earth change. A problem with spaced-based earth imagery is the presence of clouds rendering an image useless. Spending time sorting through these images take a lot of time and makes their use difficult for researchers.
My goal was to ease this process by writing software that can identify clouds in images taken specifically over Greenland for the purpose of glacial termini tracing. I worked with a glaciologist based at the University of Bristol and data from project GO-LIVE to accomplish this. In summary I used GO-LIVE’s data on how well images correlated with each other to build cloud masks for images. These masks can then be averaged over a specific area to determine if clouds are covering the landform of interest in the image. This is described in more detail in the poster above.
A NASA researcher presenting on CO2 emissions
The following day I manned my poster at the main session (first picture in this post). After presenting in the morning, I was free to look at other’s research and explore the exhibit hall. I met other high school students doing everything from analyzing soil’s effect on plant growth to predicting solar flares using tensor flow and machine learning. The exhibition hall was full of presentations from big companies like NASA and private companies selling research equipment.
After a busy couple of days, it was time to say goodbye to San Francisco.