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Line 18: + − [[File:Iuu ai.png|center|frameless|606x606px]] − Supported by the 2020 – 2021 Results Fund, a Proof of Concept (POC) was developed for the automated detection of vessel fishing activity using an AI model. The resulting predictive model takes vessel movement tracks as input and outputs the vessel tracks annotated with its activity i.e. fishing or not-fishing, as illustrated below. − [[File:FishingDetectionModel.png|center|frameless|674x674px|'''Predictive model for detecting fishing behaviour: input and output''']]+ − + + + + + +
→The Solution
== The Solution ==
== The Solution ==
[[File:Iuu ai.png|frameless|442x442px|alt=|right]]
Vessel tracking data such as Automatic Identification System (AIS) data and Vessel Monitoring System (VMS) data can provide insight into vessel movements. AI algorithms have the ability to analyse vessel movements data to reavel patterns of fishing activities and behavior. The main idea is that vessel speed and course can be useful indicators to identify behavioural markers of fishing. Eventually, the goal is to create a Maritime E-Surveillance System, powered by AI, to support to maritime surveillance of fishing activities / vessel activities. Such system can give Canadian fishery officers a bird’s eye view over what is happening on the water and provide them with the required insights enabling them to manage effectively their enforcement efforts. The diagram below explains the high level overview of such system.
Vessel tracking data such as Automatic Identification System (AIS) data and Vessel Monitoring System (VMS) data can provide insight into vessel movements. AI algorithms have the ability to analyse vessel movements data to reavel patterns of fishing activities and behavior. The main idea is that vessel speed and course can be useful indicators to identify behavioural markers of fishing. Eventually, the goal is to create a Maritime E-Surveillance System, powered by AI, to support to maritime surveillance of fishing activities / vessel activities. Such system can give Canadian fishery officers a bird’s eye view over what is happening on the water and provide them with the required insights enabling them to manage effectively their enforcement efforts. The diagram below explains the high level overview of such system.
Supported by the 2020 – 2021 Results Fund, a Proof of Concept (POC) was developed for the automated detection of vessel fishing activity using an AI model. The resulting predictive model takes vessel movement tracks as input and outputs the vessel tracks annotated with its activity i.e. fishing or not-fishing, as illustrated below. The model uses features such as vessel speed standard deviation, course standard deviation, and fishing gear type to detect vessel activity.
The model uses features such as vessel speed standard deviation, course standard deviation, and fishing gear type to detect vessel activity. The insight gained from the predictive model is then combined with other data sources, such as fisheries management areas and license conditions to detect non-compliance with fisheries regulations.
[[File:FishingDetectionModel.png|center|frameless|767x767px|'''Predictive model for detecting fishing behaviour: input and output'''|alt=]]
The insight gained from the predictive model is then combined with other data sources, such as fisheries management areas and license conditions to detect non-compliance with fisheries regulations.
[[File:IUU Non compliance example.png|center|frameless|757x757px]]
In addition, another predictive model was developed to find spatial hot spots of fishing activities.
