A new study involving University of Oxford researchers has found that artificial intelligence could accelerate the search for extraterrestrial life by showing the most promising places to look. The findings have been published in Nature Astronomy. In the search for life beyond Earth, researchers have few opportunities to collect samples from Mars or elsewhere. This makes it critical that these missions target locations that have the best chance of harbouring life. In this new study, researchers demonstrated that artificial intelligence (AI) and machine learning methods can support this by identifying hidden patterns within geographical data that could indicate the presence of life.

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. A cross-disciplinary team of researchers including Jeroen Hoekendijk, a PhD student 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.

The way seals appear in aerial photos can vary significantly from one batch to the next, depending on the altitude and angle at which the photo was taken. The research team therefore evaluated robustness to such variation. In addition, to demonstrate the potential of their deep-learning model, the scientists tested their approach on a fundamentally different dataset, of a much smaller scale: images of microscopic growth rings in fishbones called otoliths. These otoliths, or hearing stones, are hard, calcium carbonate structures located directly behind a fish's brain. The scientists trained their model to count the daily growth rings visible in the images, which are used to estimate the age of the fish.