How scientists are using machine learning to study the planet Posted on : Oct 28 - 2019

Geoscientists invented a machine learning tool to sift through satellite data.

Today's Earth scientists are spending less time standing in fields collecting soil samples, and more time behind a computer screen. Most geoscience data is automatically collected by sensors and satellites. The big challenge is making sense of all that data so that scientists can get back to what they do best: Observing the world, asking questions, conducting experiments, and finding evidence.

Scientists use large, publicly available datasets from government programs such as NASA, NOAA, and USGS (that's the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and US Geological Survey, in non-acronym speak). Many Earth scientists also have private sources, and combining these public and private datasets is difficult and time-consuming.

If a scientist wants to look at satellite images to gain a better understanding of climate change, for example, they have to spend hours sifting through data and managing several software programs.

"You want to reduce the time that you're just managing data and get to those real meaty scientific questions," says Dr. Annie Burgess. She is the lab director at the Earth Science Information Partners (ESIP) Lab, which funded a project led by Dr. Ziheng Sun, Principal Investigator, Center for Spatial Information Science and Systems at George Mason University. He developed Geoweaver, a program that solves the big data challenges that earth scientists face.

Sun developed a web-based system for deep learning on multiple datasets. It provides geoscientists with a system for making sense of public data (such as satellite images from NASA and NOAA) and private data (such as field observations). The project, called Geoweaver, helps earth scientists effectively use machine learning to sift through data so they can understand what's really going on with our planet.

"ESIP Lab Geoweaver is an online application for scientists to manage their research workflows," Sun explains. "It could be installed anywhere and accessed from anywhere. It is a life-saving project for people coding in multiple languages, dealing with multiple facilities and multiple datasets to carry out their science workflows."

Machine learning isn't new, but previous versions were too slow to support the real-time data that Earth scientists need. Today's computational power is much better, so Sun's program can train on field data in much less time. He says the old, slow versions didn't work, so geoscientists don't have faith in machine learning. That's why he created a program that combines the newest AI techniques with the programs that they already know and trust. View More