245
edits
Changes
no edit summary
Illegal, Unreported, and Unregulated fishing (IUU) has many negative environmental, economic, and social impacts.
Illegal, Unreported, and Unregulated fishing (IUU) has many negative environmental, economic, and social impacts.
* It is considered a major contributor to declining fish stocks and marine habitat destruction. It is estimated that one in every five fish sold is caught illegally. This represents up to 26 million tonnes of fish caught annually, valued at between $10 to $23 billion USD <ref>https://www.dfo-mpo.gc.ca/international/isu-iuu-eng.htm</ref>.
* IUU is considered a major contributor to declining fish stocks and marine habitat destruction. It is estimated that one in every five fish sold is caught illegally. This represents up to 26 million tonnes of fish caught annually, valued at between $10 to $23 billion USD <ref>https://www.dfo-mpo.gc.ca/international/isu-iuu-eng.htm</ref>.
* Hotspot areas for IUU can lead to higher amounts of ghost gear as vessels fishing illegally are more likely to abandon or lose their gear. Ghost fishing gear is considered one of the biggest threats to our oceans leading to more marine pollution which can be fatal to fish, marine mammals and other marine life <ref>https://www.dfo-mpo.gc.ca/fisheries-peches/management-gestion/ghostgear-equipementfantome/what-quoi-eng.html</ref> .
* Hotspot areas for IUU can lead to higher amounts of ghost gear as vessels fishing illegally are more likely to abandon or lose their gear. Ghost fishing gear is considered one of the biggest threats to our oceans leading to more marine pollution which can be fatal to fish, marine mammals and other marine life <ref>https://www.dfo-mpo.gc.ca/fisheries-peches/management-gestion/ghostgear-equipementfantome/what-quoi-eng.html</ref> .
== The Solution ==
== The Solution ==
[[File:Iuu ai.png|frameless|442x442px|alt=|right]]
[[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 can analyze vessel movements data to reveal patterns of fishing activities and behavior. The main idea is that vessel speed and course can be useful indicators to identify behavioral markers of fishing. Eventually, the goal is to create a Maritime E-Surveillance System, powered by AI, to support maritime surveillance of fishing activities/vessel activities. Such a 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 a system.
[[File:FishingDetectionModel.png|center|frameless|767x767px|'''Predictive model for detecting fishing behaviour: input and output'''|alt=]]
[[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.
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. An example of such functionality is shown below.
[[File:IUU Non compliance example.png|center|frameless|757x757px]]The functionality of the predictive model is complemetend by developing another predictive model to find spatial hot spots of fishing activities. For example, a heat map of highly active fishing areas across all Quebec region fisheries is shown below.
[[File:IUU Non compliance example.png|center|frameless|757x757px]]The functionality of the POC is further complemented by developing another predictive model to find spatial hot spots of fishing activities. For example, a heat map of highly active fishing areas across all Quebec region fisheries is shown below.
[[File:Hotspot.png|center|frameless|536x536px]]
[[File:Hotspot.png|center|frameless|536x536px]]
In the end, the insights gained from both models can '''give Canadian fishery officers a bird’s eye view over what is happening on the water leading to more effective management of enforcement efforts.'''
== References ==
== References ==