I've written a few times recently about a number of projects that are using drone technology to monitor vast environments. As you can perhaps imagine, with such endeavors, there is a huge amount of data generated, and while it presents rich pickings from a scientific perspective, nonetheless raises challenges about how that data can be managed. A recent study tested the role automation could play in both easing the burden on research teams and making data analysis more effective. The paper revealed that when teams are looking for optimal speed and accuracy that an approach that combines both machine and human can be the best. The researchers used the analysis of aerial images taken by camera drones in the Kuzikus wildlife reserve as their testing ground.
Two rhinos at the Kuzikus Nature Reserve in Namibia, photographed by drone. When the U.S. military needed to identify mines in a dangerous valley in Afghanistan, aerial-imagery specialist Tudor Thomas helped build a plane-based system to map it. Back in 2013, similar systems cost the military and its contractors one to five million dollars, Thomas says--and that didn't even include the cost of the plane. "It's hard to comprehend how much was getting spent just to make a simple aerial image," he says. The experience sparked an idea for a business: mapping by drone.
An algorithm highlights obvious animals in blue and possible animals in yellow. After a promising first run in Namibia, a Swiss project could aid savanna conservation using drones and automatic image analysis. To get a sense of how many animals live in a given area, game counts are typically done in real time by sharp-eyed people in vehicles. A project funded by the Swiss National Science Foundation (SNSF) uses drones and artificial intelligence (AI) to count wild animals more efficiently. "Human eyes are very good at detecting animals, but not at screening countless images. Computers can process a lot more data," explains Swiss geo-information specialist Devis Tuia, who received a personal grant from SNSF to form a lab to improve wildlife monitoring methods in places like Namibia.
The Great Elephant Census, conducted in 2014 and 2015, counted more than 350,000* elephants across 18 African countries. Human observers in small planes flew some 294,000 kilometers during more than 1,500 hours to systematically count the animals. Could a future census be managed locally, using unmanned aerial vehicles (UAVs, a.k.a. Although surveying the large animals in their individual reserves is a smaller job than the Great Elephant Census, such surveys cost managers substantial time and money. A Swiss research team recently tested a new approach to wildlife surveys.
For more than a decade, citizen science projects have helped researchers use the power of thousands of volunteers who help sort through datasets that are too large for a small research team. Previously, this data generally couldn't be processed by computers because the work required skills that only humans could accomplish. Now, computer machine learning techniques that teach the computer specific image recognition skills can be used in crowdsourcing projects to deal with massively increasing amounts of data--making computers a surprising new partner in citizen science projects. The research, led by the University of Minnesota-Twin Cities, was chosen as the cover story for the most recent issue of the British Ecological Society's scientific journal Methods in Ecology and Evolution. These camera traps are remote, independent devices, triggered by motion and infrared sensors that provide researchers with images of passing animals.