More than 700 imaging satellites are orbiting the earth, and every day they beam vast oceans
of information — including data that reflects climate change, health and poverty — to
databases on the ground.
There’s just one problem: While the geospatial data could help researchers and policymakers
address critical challenges, only those with considerable wealth and expertise can access
it.
Now, a team based at UC Berkeley has devised a machine learning system to tap the problem-solving
potential of satellite imaging, using low-cost, easy-to-use technology that could bring access
and analytical power to researchers and governments worldwide.
The study, titled “A generalizable and accessible approach to machine learning with global satellite
imagery,” was published in the journal Nature Communications.
Satellite images contain an incredible amount of data about the world, but the trick is
how to translate the data into usable insights without having a human comb through every
single image.
The researchers say they designed their system for accessibility, so that one person should
be able to run it on a laptop, without specialized training, to address their local problems.
The project was a collaboration between researchers of different disciplines, like computer science,
environmental and climate science, statistics, economics and public policy.
But they have been guided by a common interest in creating an open access tool that democratizes
the power of technology, making it usable even by communities and countries that lack
resources and advanced technical skill.
The system that emerged from the Berkeley-based research is called MOSAIKS, short for Multi-Task
Observation using Satellite Imagery & Kitchen Sinks.
It ultimately could have the power to analyze hundreds of variables drawn from satellite
data — from soil and water conditions to housing, health and poverty — at a global
scale.
The research paper details how MOSAIKS was able to replicate with reasonable accuracy
reports prepared at great cost by the U.S. Census Bureau.
It also has enormous potential in addressing development challenges in low-income countries
and to help scientists and policymakers understand big-picture environmental change.
The satellite data could give researchers deep new insights into expansive rangeland
areas such as the Great Plains in the U.S. and the Sahel in Africa, or into areas such
as Greenland or Antarctica that may be shedding icebergs as temperatures rise.
These areas are so large, and to have people sitting there and looking at pictures and
counting icebergs is really inefficient.
But with MOSAIKS, we could automate that and track whether these glaciers are actually
disintegrating faster, or whether this has been happening all along.
For a government in the developing world, the technology could help guide even routine
decisions, such as where to build roads.
The growing fleet of imaging satellites beam data back to Earth 24/7 — some 80 terabytes
every day, a number certain to grow in coming years.
But often, imaging satellites are built to capture information on narrow topics — supplies
of fresh water, for example, or the condition of agricultural soils.
And the data doesn’t arrive as neat, orderly images, like snapshots from a photo shop.
It’s raw data, 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.
Distilling the layers of data embedded in the images requires additional computing power
and advanced human expertise to tease out strands of information that are coherent and
useful to other researchers, policymakers or funding agencies.
Inevitably, exploiting satellite images is largely limited to scholars or agencies in
wealthy nations.
In low-income countries, one dimension of poverty is a poverty of data.
But even communities in the U.S. and other developed countries usually don’t have ready
access to geospatial data in a convenient, usable format for addressing local challenges.
Machine learning opens the door to solutions.
In a general sense, machine learning refers to computer systems that use algorithms and
statistical modeling to learn on their own, without step-by-step human intervention.
What the new research describes is a system that can assemble data delivered by many satellites
and organize it in ways that are accessible and useful.
There are precedents for such systems: Google Earth Engine and Microsoft’s Planetary Computer
are both platforms for accessing and analyzing global geospatial data, with a focus on conservation.
But, even with these technologies, considerable expertise is often required to convert the
data into new insights.
The goal of MOSAIKS is not to develop more complex machine learning systems.
Rather, its innovation is in making satellite data widely useable for addressing global
challenges.
The team did this by making the algorithms radically simpler and more efficient.
MOSAIKS starts with learning to recognize minuscule patterns in the images — the researcher
compares it to a game of Scrabble, in which the algorithm learns to recognize each letter.
In this case, however, the tiles are minuscule pieces of satellite image, 3 pixels by 3 pixels.
But MOSAIKS doesn’t conclude “this is a tree” or “this is pavement.”
Instead, it recognizes patterns and groups them together.
It learns to recognize similar patterns in different parts of the world.
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.
In a sense, MOSAIKS could do for satellite databases what Google in the early days did
for the Internet: map the data, make it accessible and user-friendly at low cost, and perhaps
make it searchable.