Latest Use of AI and Big Data for Wildlife

Latest Use of AI and Big Data for Wildlife
Latest Use of AI and Big Data for Wildlife | Credit:


A group of specialists working in AI and big data for wildlife and animal ecology has proposed a new, cross-disciplinary method to improving wildlife research and making better use of the massive volumes of data already being collected owing to AI and big data for wildlife technologies. Their findings were published today in the journal Nature Communications.

Role of Big Data and AI:

Animal ecology has entered the age of big data and the Internet of Things. Thanks to advanced technology such as satellites, drones, and terrestrial equipment such as autonomous cameras and sensors put on animals or in their environs, unprecedented volumes of data on wildlife populations are now being gathered. These data have become so simple to collect and disseminate that they have reduced distances and time required for researchers while limiting the disruptive presence of people in natural ecosystems. A number of AI and big data for wildlife tools are available today to evaluate massive datasets, but they are frequently broad in character and unsuitable for studying the particular behavior and appearance of wild animals.

A group of scientists from EPFL and other institutions has proposed a novel strategy to resolving the challenge and developing more accurate models by integrating developments in computer vision with ecologists’ knowledge. Their findings shed new light on the application of artificial intelligence to aid in the conservation of animal species.

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Cross-Disciplinary Know-How:

The scope of AI and big data for wildlife studies has expanded from the local to the worldwide. Modern technology today provides groundbreaking new methods for producing more precise estimates of wildlife populations, better understanding animal behavior, combating poaching, and halting biodiversity reduction. Using massive datasets, ecologists may utilize AI, notably computer vision, to extract essential elements from pictures, videos, and other visual forms of data in order to efficiently identify wildlife species, count individual animals, and glean crucial information. The generic programmes now used to handle such data frequently operate as black boxes and do not take full advantage of existing knowledge about the animal kingdom.

Furthermore, they are difficult to configure, can suffer from poor quality control, and may be susceptible to ethical concerns due to the handling of sensitive data. They also have various biases, particularly regional biases; for example, if all of the data used to train a certain software was collected in Europe, the programme may not be useful for other parts of the world.

Getting the Word Out About Existing Initiatives:

Tuia, Mathis, and others addressed their research issues at numerous conferences over the last two years, and the concept of strengthening linkages between computer vision and ecology emerged. They recognised that such collaboration may be tremendously beneficial in averting the extinction of some animal species. A number of projects in this area have already been launched; some of these are included in the Nature Communications article. Tuia and his colleagues at EPFL, for example, have created a software that can detect animal species based on drone photographs. It was recently tried on a seal population.

Meanwhile, Mathis and her colleagues have released DeepLabCut, an open-source software programme that enables scientists to estimate and monitor animal positions with surprising precision. It has already received 300,000 downloads. DeepLabCut was created for lab animals, however it may also be used on other species. Researchers at other institutions have built programmes as well, but it is difficult for them to share their findings because no meaningful community in this field has yet to exist. Other scientists are frequently unaware that these programmes exist, let alone which one would be appropriate for their unique research.

Concluding Thoughts:

“A community is steadily taking shape. So far we’ve used word of mouth to build up an initial network. We first started two years ago with the people who are now the article’s 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.” Prof. Devis Tuia

Frequently Asked Questions (FAQs):

How can AI help wildlife?

Artificial intelligence-enabled robots, or drones with picture databases and processing, can assist wildlife conservation authorities in keeping track of the animal population. The technology employed in AI-enabled drones can recognize the kind and species of animals and notify researchers about their daily activities.

Why is protecting wildlife important?

By protecting wildlife, we ensure that future generations will be able to appreciate our natural environment and the amazing animals that inhabit it. To assist safeguard wildlife, it’s critical to understand how species interact within their habitats and how environmental and human factors affect them.

How AI can help humans save the planet’s biodiversity?

The groundbreaking advancements in Artificial Intelligence (AI) have unlocked the potential to swiftly scan a wide range of signals, properly identify hazards from them, and give conservationists with real-time alarms.

Source: Science Daily

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