In the previous blog, I talked about upcoming changes to US AI policy with a new administration. Part of that change is a renewed focus on harnessing this technology for sustainability. Here I will showcase an example of green tech – how machine learning models are helping researchers detect illegal logging and burning in the vast Amazon rainforest. This is an exciting development and one more example of how AI can work for good.
The problem
Imagining trying to patrol an area nearly the size of the lower 48 states of dense rainforest! It is as the proverbial saying goes: finding needle in a haystack. The only way to to catch illegal activity is to find ways to narrow the surveilling area. Doing so gives you the best chances to use your limited resources of law enforcement wisely. Yet, how can that be done?
How do illegal logging and burning happen in the Amazon? Are there any patterns that could help narrow the search? Fortunately, there is. A common trait for them is happening near a road. In fact, 95% of them occur within 6 miles from a road or a river. These activities require equipment that must be transported through dense jungle. For logging, lumber must be transported so it can be traded. The only way to do that is either through waterways or dirt roads. Hence, tracking and locating illegal roads go along way to honing in areas of possible illegal activity.
While authorities had records for the government-built roads, no one knew the extent of the illegal network of roads in the Amazon. To attack the problem, enforcing agencies needed richer maps that could spot this unofficial web. Only then could they start to focus resources around these roads. Voila, there you have, green tech working for preserving rather than destroying the environment.
An Ingenious solution
In order to solve this problem, Scientist from Imazon (Amazon’s Institute of Humans and the Environment) went to work in a search for ways to detect these roads. Fortunately, by carefully studying satellite imagery they could manually trace these additional roads. In 2016 they completed this initial heroic but rather tedious work. The new estimate was now 13 times the size of the original! Now they had something to work with.
Once the initial tracing was complete, it became clear updating it manually would be an impossible task. These roads could spring up overnight as loggers and ranchers worked to evade monitoring. That is when they turned to computer vision to see if it could detect new roads. The initial manual work became the training dataset that taught the algorithm how to detect these roads from the satellite images. In supervised learning, one must first have a collection of data that shows the actual target (labels) to the algorithm (i.e: an algorithm to recognize cats must first be fed with millions of Youtube videos of cats to work).
The result was impressive. At first, the model achieved 70% accuracy and with some additional processing on top, it increased to 90%. The research team presented their results in the latest meeting of the American Geophysical Union. They also plan to share their model with neighboring countries so they can use it for their enforcement of the Amazon in areas outside Brazil.
Reflection
Algorithms can be effective allies in the fight for preserving the environment. As the example of Imazon shows, it takes some ingenuity, hard work, and planning to make that happen. While a lot of discussions around AI quickly devolve into cliches of “machines replacing humans”, this example shows how it can augment human problem-solving abilities. It took a person to connect the dots between the potential of AI for solving a particular problem. Indeed the real future of AI may be in green tech.
In this blog and in our FB community we seek to challenge, question and re-imagine how technologies like AI can empower human flourishing. Yet, this is not limited to humans but to the whole ecosystem we inhabit. If algorithms are to fulfill their promise, then they must be relevant in sustainability.
How is your work making life more sustainable on this planet?
