Humans are racing to harness the ocean's vast potential to power global economic growth. Worldwide, ocean-based industries such as fishing, shipping and energy production generate at least US 1.5 trillion in economic activity each year and support 31 million jobs. This value has been increasing exponentially over the past 50 years and is expected to double by 2030. Transparency in monitoring this "blue acceleration" is crucial to prevent environmental degradation, overexploitation of fisheries and marine resources, and lawless behavior such as illegal fishing and human trafficking. Open information also will make countries better able to manage vital ocean resources effectively. But the sheer size of the ocean has made tracking industrial activities at a broad scale impractical – until now.

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data plays a significant role in tracking vessel activity and mapping mobility patterns such as those found in fishing. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology we show how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry highlighting the changes in the vessel's moving pattern which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. In this context, we propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall $F$-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the trajectory in time and observing their inherent geometry.

The world's fish stocks are in decline and our increasing demand for seafood may be one of the main drivers. But the true extent of the problem is hard to estimate, especially when fishing occurs in the high seas, which lie beyond national jurisdiction and are hard to monitor. Conservation planners face growing pressures to combat illegal, unregulated and unreported (IUU) fishing, the value of which has been estimated at US$10-23.5 billion annually. This is an important cost for society as a whole, but also for the major high seas fishing countries such as China and Taiwan that subsidize their fleets and may have low labour costs. Artificial intelligence (AI) could address this global environmental concern -- and satisfy the need of seafood retailers and consumers to know if what they're selling and eating is sustainable.

Researchers are learning more than ever before about the effects humans are having on global fish stocks. It's all thanks to a website -- funded in part by actor Leonardo DiCaprio's foundation -- that tracks ships and uses a type of artificial intelligence to figure out incredible detail in worldwide fishing patterns. Kristina Boerder, a PhD student in marine biology at Dalhousie University, is one of the researchers working with Global Fishing Watch and a co-author on a study published this week in the journal Science. She said humans have been fishing for 42,000 years but we've been "rather in the dark" about where and how much fishing activity is happening. "This is really a problem because this is a resource that is not infinite," Boerder told the CBC's Mainstreet.

Summary: As deep learning expands those capabilities are finding their way into the not-for-profit community in the service of conserving the earth's wildlife and forests. The for-profit world may be driving AI but it's a solution to many problems in the not-for-profit world as well. We were particularly impressed by the use of deep learning technologies to solve problems in the pursuit of preserving natural resources including many species of animals and fish, and also including forests. For the most part the data problems that nature conservancy organizations face fall into these categories. Going back 20 years this meant putting intrepid feet on the ground with binoculars and note pads.

Summary: As deep learning expands those capabilities are finding their way into the not-for-profit community in the service of conserving the earth's wildlife and forests. The for-profit world may be driving AI but it's a solution to many problems in the not-for-profit world as well. We were particularly impressed by the use of deep learning technologies to solve problems in the pursuit of preserving natural resources including many species of animals and fish, and also including forests. For the most part the data problems that nature conservancy organizations face fall into these categories. Going back 20 years this meant putting intrepid feet on the ground with binoculars and note pads.