Unveiling a revolutionary stride in machine learning, we delve into the transformative potential of satellite imagery. Discover how this cutting-edge technology is reshaping our world, enhancing human lives on a global scale, and redefining the boundaries of possibility.
Berkeley-based project could support action worldwide on climate, health, and poverty
A new system developed in research at the University of California, Berkeley, uses machine learning to drive low-cost, easy-to-use technology that one person could run on a laptop, without advanced training, to address their local problems.
- The system was developed as a collaboration between the Global Policy Lab, which Hsiang directs, and Benjamin Recht’s research team in the department of Electrical Engineering and Computer Sciences.
Machine learning opens the door to solutions
The goal of MOSAIKS is not to develop more complex machine learning systems, but to make satellite data widely useable for addressing global challenges
- It starts with learning to recognize minuscule patterns in the images
- When thousands of terabytes from hundreds of sources are analyzed and organized, researchers can choose a village or a country or a region and draw out organized data that can touch on themes as varied as soil moisture, health conditions, human migration and home values
Creating a living atlas of global data
Hsiang imagines the data being collected into computer-based, continually evolving atlases
- Turn to any given “page” and a user could access broad, deep data about conditions in a country or a region
MOSAIKS: Improving lives, protecting the planet
Geospatial imaging holds enormous potential for developing nations to address challenges related to agriculture, poverty, health and human migration, scholars at UC Berkeley say.
- Until now, the technology and expertise needed to efficiently access and analyze satellite data usually has been limited to developed countries.
- With OSSIKS, researchers say, it could have the power to analyze hundreds of variables drawn from satellite data at a global scale.
The challenge: Organizing trillions of bytes of raw satellite data
The growing fleet of imaging satellites beam data back to Earth 24/7 – some 80 terabytes every day, according to the research
- Often, imaging satellites are built to capture information on narrow topics – supplies of fresh water, for example, or the condition of agricultural soils
- Raw data is a mass of binary information
- Researchers who access the data have to know what they’re looking for
- Merely storing so many terabytes of data requires a huge investment
