Chinese fishing is increasing security risks near South Korea's tense nautical border, said a top Cabinet member in Seoul, pledging to deploy advanced technology to crack down on illegal trawling. Minister of Oceans and Fisheries Moon Seong-hyeok said in an interview that illegal fishing must be "completely eradicated," joining in calls from across Asia to end what many see as Beijing's assertive push into regional waters. South Korea has long complained about Chinese trawlers operating in the Yellow Sea -- what Koreans call the West Sea -- near its islands off the coast of North Korea. "When it comes to illegal fishing, whether it be foreign or domestic vessels, we will crack down," Moon told Bloomberg News on Friday, saying South Korea will from next year increase its maritime surveillance systems using drones at sea and artificial intelligence. South Korea, which lists the U.S. as its main military ally and China as its biggest trading partner, turned up the pressure on Beijing over the weekend when it won from Washington a termination of bilateral missile guidelines that have long restricted Seoul's development of missiles to under the range of 800 kilometers (500 miles).

Many of the world's most important fisheries are experiencing increases in illegal fishing, undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending illegal, unreported, and unregulated (IUU) fishing is improving our ability to identify whether a vessel is fishing illegally and where illegal fishing is likely to occur in the ocean. However, monitoring the oceans is costly, time-consuming, and logistically challenging for maritime authorities to patrol. To address this problem, we use vessel tracking data and machine learning to predict illegal fishing on the Patagonian Shelf, one of the world's most productive regions for fisheries. Specifically, we focus on Chinese fishing vessels, which have consistently fished illegally in this region. We combine vessel location data with oceanographic seascapes -- classes of oceanic areas based on oceanographic variables -- as well as other remotely sensed oceanographic variables to train a series of machine learning models of varying levels of complexity. These models are able to predict whether a Chinese vessel is operating illegally with 69-96% confidence, depending on the year and predictor variables used. These results offer a promising step towards preempting illegal activities, rather than reacting to them forensically.

Researchers using artificial intelligence and "big data" plan to develop new algorithms that they say will enable them to identify, locate – and eventually predict – crimes committed in the world's oceans, from illegal fishing off the Patagonia shelf to drug smuggling in Central America to slave labor and human trafficking in the Indian Ocean. The perpetrators of these illegal, unreported and unregulated (IUU) activities collectively use vessels called "the dark fleet," not just because of their criminal activity, but because they try to hide their location by turning off their GPS tracking systems and navigating between legally operating and visible boats. "IUUs include all kinds of terrible things," said James Watson, a marine scientist expert at Oregon State University, and a principal investigator on the project. "We came into this thinking primarily about illegal fishing, but that turns out to be just the tip of the iceberg. It is much, much bigger."

Drones aren't just cracking down on land-based poaching in Africa -- ATLAN Space is launching a pilot that will use autonomous drones to report illegal fishing in the Seychelles islands. The fliers will use computer vision to identify both the nature of boats in protected waters as well as their authorization. If they detect illegal fishing boats, the drones will note vessel locations, numbering and visible crews, passing the information along to officials. The pilot starts in October. The technology won't be limited to any specific drone system, ATLAN Space added, and that's important for the fishing industry.

I recently read an article (paywall) in the WSJ about Paul Allen's Vulcan initiative to curb illegal fishing. It's insightful and sheds light on Big Data techniques to address societal problems. After thinking on the story, it struck me that it could be used as a pedagogical tool to synthesize data science with domain knowledge. To me, this stands as the biggest limitation of what I refer to as'data science thinking'– letting technical skills drive the analysis, only later incorporating domain understanding. This post somewhat reads like a case note from business school and the idea is to get data scientists, product managers and engineers talking earlier on in the process.

From smarter electric grids to automated monitoring of at-risk environments, there are many areas where technology could have exponential effects on sustainability. If the world's natural resources are increasingly stressed and depleted, the silver lining may be that we're becoming better equipped at tracking that destruction and potentially doing something about it. Cheap, widespread sensor networks, the internet of things, magnitude-improvements in computing power, open source algorithms–these all allow us to manage oceans and forests more effectively, if we want the opportunity. Artificial intelligence systems that can sense, think, learn, and act on their own could allow a major upgrade in conservation efforts, in dealing with climate change, and living in a more energy-efficient manner. A report released during the recent Davos World Economic Forum meeting laid more than 80 potential environmental applications for AI, ranging from the mundane to the futuristic.

Illegal, Unreported and Unregulated (IUU) fishing is becoming a major issue around the world . In general, IUU fishing is a broad term encapsulating many different scenarios (i.e. For the purposes of this blog, we'll just limit out discussion to Illegal fishing – i.e. fishing uses practices that are against the law, fishing in areas where it is not allowed, or taking animals which are not allowed to be taken. In this blog, I'm simply going to present some code demonstrating the simulation of a training dataset. The training dataset consists of 3000 fictional ships that engage in fishing activities. First, let's load up the libraries and set variables with our base categories Create names for our 3000 ships here.

Illegal, Unreported and Unregulated (IUU) fishing is becoming a major issue around the world . In general, IUU fishing is a broad term encapsulating many different scenarios (i.e. For the purposes of this blog, we'll just limit out discussion to Illegal fishing – i.e. fishing uses practices that are against the law, fishing in areas where it is not allowed, or taking animals which are not allowed to be taken. In this blog, I'm simply going to present some code demonstrating the simulation of a training dataset. The training dataset consists of 3000 fictional ships that engage in fishing activities. First, let's load up the libraries and set variables with our base categories Create names for our 3000 ships here.

Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy. Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat's haul includes endangered and protected fish.

Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy. Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat's haul includes endangered and protected fish.