Published Jul 28, 2023 by Jim Tatum However, until fairly recently, the default monitoring process largely consisted of real people monitoring video screens in real time, for designated periods of time, manually recording what they observed, and attempting to extrapolate the needed data from those observations.
For example, the University of Alaska participated in a study regarding installation of two power generating turbines in the Kvichak River, near the town of Igiugig, AL. The remote town had for years produced all its energy with diesel fuel that had to be flown into the town, making the cost of electricity very expensive. The town, and the state of Alaska, wanted to know if power generated by underwater turbines would be a viable, cost-efficient solution.
However, the river, which is pristine, is one of the largest salmon runs in the world, and many people in the state rely on the fishing economy as a vital part of their livelihood. They wanted to ensure that the turbines would not negatively impact the fish, or otherwise cause any unintended environmental consequences.
Two turbines were placed under the water on the riverbed. MarineSitu deployed a camera system near the turbines, and UA scientists physically monitored and gathered data.
“They basically had people working round the clock, monitoring a computer screen for six-hour shifts at a time, and they literally hand counted fish they observed on the screen,” Joslin says.
Both Joslin and Plainsight Co-Founder and Chief Product Officer Elizabeth Spears note that, while the data gathering and analysis was conducted as thoroughly as possible, it seems clear that such a process will benefit greatly from AI-assisted models.
“It is very labor intensive. In the past, for marine environments, monitoring was available only in snapshots,” Spears says. “With the AI models, it’s a night and day difference in the quality and level of monitoring you can do.”
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