Few parts of our planet are more vital or more threatened than our oceans. They cover 70% of the Earth’s surface and house around 80% of animal life – all while absorbing about 90% of the heat from carbon emissions. 

With enterprises largely agreeing that AI is key for addressing climate change, computer vision looks poised to offer deep insights into ocean ecosystems, the communities around them, and how they impact the planet at large. With models for tracking vulnerable aquatic populations, identifying ocean plastic and other waste, and more, computer vision is already proving vital to protecting all types of waterways and supporting innovation in marine industries.

What Is the Blue Economy? 

The United Nations has compiled several official definitions for the Blue Economy. Organizations ranging from the World Bank to the Commonwealth of Nations may differ in the specific terms they use, but they all describe it as an economy focused on responsible stewardship over the planet’s oceans and sustainable use of their resources. From sustainable fisheries to maritime renewable energies, a strong Blue Economy’s goals also include improving livelihoods and creating jobs around the world while mitigating the negative impact on marine ecosystems. 

Forbes reports that the Blue Economy is huge and growing. In 2018, it supported more than 2 million jobs and contributed $373 million to the nation’s gross domestic production across its various sectors. Over the next decade, experts predict the total value of the global Blue Economy to double and exceed $3 trillion.

Among the key challenges facing the evolving Blue Economy is the need to foster strong partnerships that transcend both industry and national borders. Last year, the National Oceanic and Atmospheric Administration (NOAA) outlined a five-year plan for strengthening the Blue Economy by “maximiz[ing] sustainable economic contributions of our ocean, coastal, and Great Lakes resources.” Encompassing initiatives related to transportation, farming, tourism, and more, the NOAA’s plan makes note of the crucial role technology will play. 

Computer Vision Use Cases for the Blue Economy

Our oceans remain largely mysterious, filled with undiscovered species, unknown resources, and untapped opportunities. Researchers are similarly just beginning to understand the ways computer vision can be applied to generate valuable insights and inspire transformative change.

Photo Credit: NOAA Fisheries

Detecting, Tracking, and Protecting Animal Life

The transition to renewable energy is not without its own challenges and environmental risk factors. In Germany, the construction of wind turbines recently threatened several species of endangered birds. Officials are hopeful that cameras supplemented with computer vision technology can help create an automated alert system to recognize the presence of birds and slow or stop blade rotation. In the growing world of Marine Energy, similar solutions can work to detect and protect aquatic animal populations from underwater turbines. 

Underwater turbines have the potential to help diversify our renewable energy sources, reducing dependence on high-polluting fuels and contributing to a cleaner planet. The Department of Energy is investing in improving the efficacy and cost effectiveness of these nascent technologies, as well as the development of complementary technology solutions to ensure underwater turbines are operated safely—for humans and wildlife alike. 

Similar to wind turbines, underwater turbines introduce potential safety risk for underwater ecosystems. Like birds in the air, fish and other sea life have to safely navigate around these turbines. Underwater submersibles akin to submarine drones equipped with computer vision  are among the technologies being developed to monitor these environments. With these machines and their impressive computer vision capabilities, energy providers can automate alerts to keep aquatic life safe, while simultaneously generating insights derived from computer vision to help guide construction and maintenance efforts.

In the sea and freshwater aquaculture space, computer vision presents many of the same game-changing applications as it does for on-land agriculture. Aquatic farmers can use custom models to more accurately count, track, and monitor fish in real time, even applying models for assessing an animal’s health and well-being based on subtle changes in appearance and behavior. 

Computer vision is also useful for tracking vulnerable marine and freshwater species and informing aid responses. With just around 350 remaining, North Atlantic right whales are among the most threatened marine species on the planet. NOAA researchers use machine learning algorithms to maintain a database of every right whale in the world. Computer vision speeds what would otherwise be a tedious and time-consuming process, making it possible to distinguish between near-identical whales with ease. That’s just one example of visual data analytics supporting conservation efforts. NOAA has also developed models for more easily counting seals, monitoring turtle activity, identifying whales by their calls, and more

Keeping Oceans and Other Waterways Clean

Plastic waste alone represents a massive problem for the world’s waterways. Traditional manual approaches simply are not sufficient for addressing the growing issue. Current methods for monitoring and quantifying marine plastic often rely on manual use of nets known as manta trawls. Like other manual collection processes, trawling the surface for plastic necessitates hours of hands-on work and significant costs. 

Some high-profile cleanup efforts make use of enormous nets that introduce issues of their own. The Dutch nonprofit, Ocean Cleanup, for example, leverages a three-meter-deep net that needs two ships to tow it. This system was devised after an initial collection apparatus proved ineffective. Ocean Cleanup’s first attempts fell short when its nets suffered damage from waves and wind. Even the organization’s new and improved methods are not without their shortcomings. Critics have pointed to its use of fossil fuel-burning ships and noted that its methods may threaten marine animals, including those in the mysterious neuston ecosystem around the water’s surface. 

Computer vision’s potential usefulness for ocean and river cleanups is clear. Writing for Towards Data Science, Gautam Tata showcases how an object detection model can optimize processes for identifying, quantifying, and managing ocean plastic. Without an existing labeled dataset, Tata created one of his own, focusing on the epipelagic layer (from just below the water’s surface to about 100 meters deep). His efforts resulted in a model capable of identifying ocean plastic with 85% accuracy even in instances of occlusion and poor light conditions. 

Dive Into Your Visual Data

Plainsight is passionate about working with enterprises to identify environmental challenges and sustainable solutions while empowering enterprises to achieve goals and ensure compliance.  Learn more about what taking a dive into your visual data for deeper insights could reveal and how Plainsight’s proven approach can transform business and protect our world.

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