AI Saves Lives

Artificial Intelligence logistics and deep learning skills are making their way into the non-profit community to help save Earth's species and forests. AI solves many challenges in the non-profit sector. People are particularly happy with the use of deep learning technologies to solve problems related to the preservation of natural resources, such as many species of animals and fish as well as forests.

The majority of the data issues that wildlife conservation groups confront fall into these categories:


● Counting the quantity of a species in a certain place and monitoring changes over time.
● Detecting unlawful behavior such as poaching, illicit fishing or logging.

Back in the day, this required placing brave feet on the ground with binoculars and note pads. Some onsite labor could be inferred from satellite imagery ten years ago, but that still needed intense human analytic effort and a goal visible from space. Robotic cameras provided some closeup data, but they only collected information from where they were directed. They remained pricey, and they still required human monitoring and maintenance.

For non-profits, the issue often comes down to finances. They pay for surveys, ships at sea and at least modest stipends for the hundreds of unpaid employees who collect and evaluate data. Researchers can use artificial intelligence applications such as emerging deep learning technologies, notably image processing and natural language processing, to greatly expand their reach. Here are some examples of artificial intelligence solutions.

Saving Endangered Sea Birds and Frogs

Conservation Measures (CMI) based in Santa Cruz is focused on developing automated alternatives to previously labor-intensive wildlife survey activities to reduce costs while increasing the size and efficacy of wildlife metrics. Deep learning audio processing was used to overcome the problem with seabird population census programs all over the world.

The analytics can correctly estimate how many birds are in a certain region by using remote audio sensors to capture the noises of a seabird population. They recently conducted a census of endangered California red-legged frogs using a similar method applied to a much smaller geographic area.

Undertaking the Great Elephant Census

Vulcan Inc. is a multi-faceted platform founded to accomplish numerous charitable interests. It contains the Allen Institute for Artificial Intelligence which powers many of Vulcan's projects using analytics. They recently took the lead in a satellite-based assessment of elephant numbers in 18 African countries. Image processing made the task feasible Unfortunately, they discovered that the population had dropped by 30% in just seven years.

Detecting Illegal Fishing

Global Fishing Watch uses satellite data in near real-time to detect illegal fishing activities. All commercial ships and many recreational boats now have an AIS system, which permits exact tracking of any vessel through satellite. Deep learning image processing can recognize ship movement patterns that suggest a vessel is most likely fishing, or that the nationality of the vessel is not allowed to fish in certain waters, and maps them against restricted regions.

Protecting World Forests

Orbital Insight, a satellite intelligence business, has worked with Worldwide Forest Watch to detect illicit logging and other drivers of global forest degradation. Although GFW had been in operation since 1997, the vast area impeded its capacity to identify irregularities that may suggest anything from illegal logging to fires to be monitored and the complexity of detecting changes in near real-time. With the integration of Orbital Insight imaging and image processing, changes to be addressed can now be detected extremely quickly.

GFW makes its data available to anybody, and conservation organizations, the press and national actors are utilizing it to help safeguard diminishing forests.

Artificial Intelligence Landscape

The investing community has separated the AI landscape into application sectors such as healthcare, logistics and manufacturing. It's also worth noting that there's a category called "For Good," and all samples above originate from there. There may not be a flood of investment in this area, but it's comforting to know that some of our efforts in data science are benefiting the world.

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