{"id":353472,"date":"2022-02-10T13:14:52","date_gmt":"2022-02-10T12:14:52","guid":{"rendered":"https:\/\/innovationorigins.com\/?post_type=selected&p=353472"},"modified":"2022-02-10T13:14:52","modified_gmt":"2022-02-10T12:14:52","slug":"artificial-intelligence-and-big-data-can-help-preserve-wildlife","status":"publish","type":"selected","link":"https:\/\/ioplus.nl\/archive\/en\/selected\/artificial-intelligence-and-big-data-can-help-preserve-wildlife\/","title":{"rendered":"Artificial intelligence and big data can help preserve wildlife"},"content":{"rendered":"\n

A team of experts in artificial intelligence and animal ecology has put forth a new, cross-disciplinary approach intended to enhance research on wildlife species and make more effective use of the vast amounts of data now being collected thanks to new technology, as announced in a press release<\/a> by \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), a Swiss technology institute, which contributed to the study. The results were published in Nature Communications.<\/a><\/p>\n\n\n\n

Big Data meets big game<\/h3>\n\n\n\n

The field of animal ecology has entered the era of big data and the Internet of Things. Unprecedented amounts of data are now being collected on wildlife populations, thanks to sophisticated technology such as satellites, drones and terrestrial devices like automatic cameras and sensors placed on animals or in their surroundings. These data have become so easy to acquire and share that they have shortened distances and time requirements for researchers while minimizing the disrupting presence of humans in natural habitats. Today, a variety of AI programs are available to analyze large datasets, but they\u2019re often general in nature and ill-suited to observing the exact behavior and appearance of wild animals. A team of scientists from EPFL and other universities has outlined a pioneering approach to resolve that problem and develop more accurate models by combining advances in computer vision with the expertise of ecologists. Their findings open up new perspectives on the use of AI to help preserve wildlife species.<\/p>\n\n\n\n

Cross-disciplinary know-how<\/strong><\/h3>\n\n\n\n

Wildlife research has gone from local to global. Modern technology now offers revolutionary new ways to produce more accurate estimates of wildlife populations, better understand animal behavior, combat poaching and halt the decline in biodiversity. Ecologists can use AI, and more specifically computer vision, to extract key features from images, videos and other visual forms of data in order to quickly classify wildlife species, count individual animals, and glean certain information, using large datasets. The generic programs currently used to process such data often work like black boxes and don\u2019t leverage the full scope of existing knowledge about the animal kingdom. What\u2019s more, they\u2019re hard to customize, sometimes suffer from poor quality control, and are potentially subject to ethical issues related to the use of sensitive data. They also contain several biases, especially regional ones; for example, if all the data used to train a given program were collected in Europe, the program might not be suitable for other world regions.<\/p>\n\n\n\n

\u201cWe wanted to get more researchers interested in this topic and pool their efforts so as to move forward in this emerging field. AI can serve as a key catalyst in wildlife research and environmental protection more broadly,\u201d says Prof. Devis Tuia, the head of EPFL\u2019s Environmental Computational Science and Earth Observation Laboratory and the study\u2019s lead author. If computer scientists want to reduce the margin of error of an AI program that\u2019s been trained to recognize a given species, for example, they need to be able to draw on the knowledge of animal ecologists. These experts can specify which characteristics should be factored into the program, such as whether a species can survive at a given latitude, whether it\u2019s crucial for the survival of another species (such as through a predator-prey relationship) or whether the species\u2019 physiology changes over its lifetime. \u201cWe used this approach to improve a bear-recognition program a few years ago,\u201d says Prof. Mackenzie Mathis, a neuroscientist at EPFL and co-author of the study. \u201cA researcher studying bear DNA had installed automatic cameras in bear habitats in order to recognize individual animals. But bears shed half of their body fat when they hibernate, meaning the generic programs she used were no longer able to recognize the bears once the season changed. We therefore added criteria to the program that can not only look at whether an animal has a given characteristic but also be tweaked manually to allow for possible deviations.\u201d<\/p>\n\n\n\n

Getting the word out about existing initiatives<\/strong><\/h3>\n\n\n\n

The idea of forging stronger ties between computer vision and ecology came up as Tuia, Mathis and others discussed their research challenges at various conferences over the past two years. They saw that such collaboration could be extremely useful in preventing certain wildlife species from going extinct. A handful of initiatives have already been rolled out in this direction; some of them are listed in the Nature Communications<\/em> article. For instance, Tuia and his team at EPFL have developed a program that can recognize animal species based on drone images. It was tested recently on a seal population. Meanwhile, Mathis and her colleagues have unveiled an open-source software package called DeepLabCut that allows scientists to estimate and track animal poses with remarkable accuracy. It\u2019s already been downloaded 300,000 times. DeepLabCut was designed for lab animals but can be used for other species as well. Researchers at other universities have developed programs too, but it\u2019s hard for them to share their discoveries since no real community has yet been formed in this area. Other scientists often don\u2019t know these programs exist or which one would be best for their specific research.<\/p>\n\n\n\n

That said, initial steps towards such a community have been taken through various online forums. The Nature Communications<\/em> article aims for a broader audience, however, consisting of researchers from around the world. \u201cA community is steadily taking shape,\u201d says Tuia. \u201cSo far we\u2019ve used word of mouth to build up an initial network. We first started two years ago with the people who are now the article\u2019s other lead authors: Benjamin Kellenberger, also at EPFL; Sara Beery at Caltech in the US; and Blair Costelloe at the Max Planck Institute in Germany.\u201d<\/p>\n\n\n\n

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