
Chameleon AI program classifies objects in satellite images faster
Just four or five high-quality images are all that’s needed to retrain the system for a new task
When it comes to classifying images, neural networks can do in the blink of an eye what humans would need hours to accomplish. These networks are trained on data that have been annotated manually – the more data fed into a neural network, the more accurate its results will be. For instance, trees and buildings can look very different depending on the region they’re found in. That means a neural network’s algorithms need to be shown many different images of these objects taken under many different conditions to be able to recognize them reliably. “The problem in environmental science is that it’s often impossible to obtain a big enough dataset to train AI programs for our research needs,” says Marc Rußwurm, previously a postdoc at EPFL and today an assistant professor at Wageningen University in the Netherlands. “That’s especially true if we want to study phenomena specific to a given region, like the extinction of an indigenous tree species, or if we want to identify objects that are statistically small in number but widely dispersed, like ocean debris.”
Another challenge in training neural networks on aerial and satellite images relates to the wide range of image resolutions and spectral bands possible, and to the type of device used (i.e., from drones and satellites). To get around this problem, METEOR was designed to be adaptable and capable of meta-learning – it essentially takes shortcuts based on tasks successfuly solved previously, but in other contexts. “We’ve developed algorithms and methods that enable neural networks to generalize the results of earlier deployments and apply that adaptation strategy to new situations,” says Rußwurm. Thanks to their novel approach, METEOR needs only four or five good images of an object to deliver sufficiently reliable results.
