Duck…crane…goose! Identify waterfowl from a bird’s eye view, and help develop technology to identify them automatically!

 

We call the project "Drones for Ducks" because it's catchy, but it's not actually all about ducks. Ducks just happen to be the most common type of aquatic bird ("waterfowl") at the wildlife refuges we are studying. Migrating waterfowl in general are important not just to one ecosystem but to many as they make their seasonal journeys across vast landscapes, sometimes even between continents! Because of this, migratory waterfowl can provide a big-picture indication of environmental conditions. People also value waterfowl for other reasons: culture, aesthetics, or for hunting. Because of this, people managing public lands like the National Wildlife Refuge System need to have accurate population counts so that they can ensure the habitat is suitable and has enough resources for all the birds, and to intervene if the population seems to be in trouble.

 

Surveying waterfowl using drones equipped with high resolution cameras is a relatively new solution to these long-standing problems. Sounds great, right? However, there's a big drawback: the sheer number of images taken from the drones. It takes a lot of time to systematically go through all of the images to determine if there are any birds to be counted, not to mention to count the birds that are present.

 

Right now, we are testing three different machine learning algorithms to determine which is the most accurate for detecting different types of birds in wetland environments in New Mexico. However, these algorithms need a lot of examples of what a "duck" or a "goose" looks like. Humans have to provide these examples to the computer; we call this training data. This is where Zooniverse users like you come in: you will be looking at images taken by drones and labeling all the birds you can see. These labels will then be given to the algorithms to teach them how to "find" birds automatically.