Artificial intelligence helps speed up ecological surveys

Example of aerial image used by the model that accelerates the seal survey. NIOZ - Scientists at EPFL, the Royal Netherlands Institute for Sea Research and Wageningen University & Research have developed a new deep-learning model for counting the number of seals in aerial photos that is considerably faster than doing it by hand. With this new method, valuable time and resources could be saved which can be used to further study and protect endangered species. Ecologists have been monitoring seal populations for decades, building up vast libraries of aerial photos in the process. Counting the number of seals in these photos require hours of meticulous work to manually identify the animals in each image. Today, a cross-disciplinary team of researchers including Jeroen Hoekendijk, a PhD candidate at Wageningen University & Research (WUR) and employed by the Royal Netherlands Institute for Sea Research (NIOZ), and Devis Tuia, an associate professor and head of the Environmental Computational Science and Earth Observation Laboratory at EPFL Valais, have come up with a more efficient approach to count objects in ecological surveys. In their study, published in Scientific Reports, they use a deep-learning model to count the number of seals in archived photos.