From Wired by Khari Johnson
FOR THE PAST three decades, geologist Carlos Souza has worked at the Brazil-based nonprofit Imazon, exploring ways he and the teams he coordinates can use applied science to protect the Amazon rainforest.
For much of that time, satellite imagery has been a big part of his job.
In the early 2000s, Souza and colleagues came to understand that 90 percent of deforestation occurs within 5 kilometers of newly created roads.
While satellites have long been able to track road expansion, the old way of doing things required people to label those findings by hand, amassing what would eventually become training data.
Those years of labor paid off last fall with the release of an AI system that Imazon says reveals 13 times more roadway than the previous method, with an accuracy rate of between 70 and 90 percent.
Proponents of satellite imagery and machine learning have ambitious plans to solve big problems at scale.
The technology can play a role in anti-poverty campaigns, protect the environment, help billions of people obtain street addresses, and increase crop yields in the face of intensifying climate change.
A UNESCO report published this spring highlights 100 AI models with the potential to transform the world for the better.
But despite recent advances in deep learning and the quality of satellite imagery, as well as the record number of satellites expected to enter orbit over the next few years, ambitious efforts to use AI to solve big problems at scale still encounter traditional hurdles, like government bureaucracy or a lack of political will or resources.
Stopping deforestation, for instance, requires more than spotting the problem from space.
A Brazilian federal government program helped reduce deforestation from 2004 to 2012 by 80 percent compared to previous years, but then federal support waned.
In keeping with an election promise, President Jair Bolsonaro weakened enforcement and encouraged opening the rainforest to industry and cattle ranch settlers.
As a result, deforestation in the Amazon reached the highest levels seen in more than a decade.
Back on land, the consulting company Capgemini is working with The Nature Conservancy, a nonprofit environmental group, to track trails in the Mojave Desert and protect endangered animal habitats from human activity.
In a pilot program last year, the initiative mapped trails created by off-road vehicles in hundreds of square miles of satellite imagery in Clark County, Nevada, to create an AI model that can automatically identify newly created roads.
Based on that work, The Nature Conservancy intends to expand the project to monitor the entirety of the desert, which stretches more than 47,000 square miles across four US states.
However, as in the Amazon, identifying problem areas only gets you so far if there aren’t enough resources to act on those findings.
The Nature Conservancy uses its AI model to inform conversations with land managers about potential threats to wildlife or biodiversity.
Conservation enforcement in the Mojave Desert is overseen by the US Bureau of Land Management, which only has about 270 rangers and special agents on duty.
In northern Europe, the company Iceye got its start monitoring ice buildup in the waters near Finland with microsatellites and machine learning.
But in the past two years, the company began to predict flood damage using microwave wavelength imagery that can see through clouds at any time of day.
The biggest challenge now, says Iceye’s VP of analytics, Shay Strong, isn’t engineering spacecraft, data processing, or refining machine learning models that have become commonplace.
It’s dealing with institutions stuck in centuries-old ways of doing things.
“We can more or less understand where things are going to happen, we can acquire imagery, we can produce an analysis.
But the piece we have the biggest challenge with now is still working with insurance companies or governments,” she says.
“It’s that next step of local coordination and implementation that it takes to come up with action,” says Hamed Alemohammad, chief data scientist at the nonprofit Radiant Earth Foundation, which uses satellite imagery to tackle sustainable development goals like ending poverty and hunger.
“That’s where I think the industry needs to put more emphasis and effort.
It’s not just about a fancy blog post and deep learning model.”
It’s often not only about getting policymakers on board.
In a 2020 analysis, a cross-section of academic, government, and industry researchers highlighted the fact that the African continent has a majority of the world’s uncultivated arable land and is expected to account for a large part of global population growth in the coming decades.
Satellite imagery and machine learning could reduce reliance on food imports and turn Africa into a breadbasket for the world.
But, they said, lasting change will necessitate a buildup of professional talent with technical knowledge and government support so Africans can make technology to meet the continent’s needs instead of importing solutions from elsewhere.
“The path from satellite images to public policy decisions is not straightforward,” they wrote.
Labaly Toure is a coauthor of that paper and head of the geospatial department at an agricultural university in Senegal.
In that capacity and as founder of Geomatica, a company providing automated satellite imagery solutions for farmers in West Africa, he’s seen satellite imagery and machine learning help decision-makers recognize how the flow of salt can impact irrigation and influence crop yields.
He’s also seen it help settle questions of how long a family has been on a farm and assist with land management issues.
Sometimes free satellite images from services like NASA’s LandSat or the European Space Agency’s Sentinel program suffice, but some projects require high-resolution photos from commercial providers, and cost can present a challenge.
“If decision-makers know [the value] it can be easy, but if they don’t know, it’s not always easy,” Toure said.
Back in Brazil, in the absence of federal support, Imazon is now forging ties with more policymakers at the state level.
“Right now, there’s no evidence the federal government will lead conservation or deforestation efforts in the Amazon,” says Souza.
In October 2022, Imazon signed cooperation agreements with public prosecutors gathering evidence of environmental crimes in four Brazilian states on the border of the Amazon rainforest to share information that can help prioritize enforcement resources.
When you prosecute people who deforest protected lands, the damage has already been done.
Now Imazon wants to use AI to stop deforestation before it happens, interweaving that road-detection model with one designed to predict which communities bordering the Amazon are at the highest risk of deforestation within the next year.
Deforestation continued at historic rates in early 2022, but Souza is hopeful that through work with nonprofit partners, Imazon can expand its deforestation AI to the other seven South American countries that touch the Amazon rainforest.
And Brazil will hold a presidential election this fall.
The current leader in the polls, former president Luiz Inácio Lula da Silva, is expected to strengthen enforcement agencies weakened by Bolsonaro and to reestablish the Amazon Fund for foreign reforestation investments.
Lula’s environmental plan isn’t expected out for a few months, but environmental ministers from his previous term in office predict he will make reforestation a cornerstone of his platform.
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