Salmon Vision BC — Indigenous-led wild salmon AI, Gitanyow Fisheries Authority, peer-reviewed in Frontiers in Marine Science 2023

Canada (BC, Central Coast)

Salmon Vision BC

Indigenous-led wild salmon AI deployment. Gitanyow Fisheries Authority + Skeena Fisheries Commission + SFU. Peer-reviewed in Frontiers in Marine Science 2023. DOI 10.3389/fmars.2023.1200408.

Lead actors + named partners

What this deployment does

Salmon Vision combines AI with age-old fishing weir technology — First Nations communities have used fish weirs for salmon monitoring for centuries. The AI integration automates species identification, count, and behavioural monitoring.

Substantive performance:

Operational locations:

Peer-reviewed status

Atlas WI, Ma S, Chou YC, Connors K, Scurfield D, Nam B, Ma X, Cleveland M, Doire J, Moore JW, Shea R, Liu J (2023). “Wild salmon enumeration and monitoring using deep learning empowered detection and tracking,” Frontiers in Marine Science 10:1200408. Published 20 September 2023. DOI: 10.3389/fmars.2023.1200408.

Public coverage: CBC News (28 Oct 2023); EurekaAlert (28 Sep 2023); AZoRobotics (2024); Innovate BC; multiple primary sources.

Why this deployment matters

Salmon Vision operationalises the integration of Indigenous-led methodology with cutting-edge AI. The fish weir is a centuries-old First Nations technology; the AI integration is the contemporary layer. The deployment is Indigenous-led and academically-anchored — substantively distinct from vendor-captured AI.

William Housty (Heiltsuk Integrated Resource Management Department Associate Director) on the integration (UBC CTLT 19 Nov 2025): “To us it is… we’re utilizing our own Traditional Knowledge to inform AI. AI is turning around and helping us to gather information, and we’re making decisions based on that information… That in itself is ethical, in that we’re not relying on the technology to make a decision for us.”

Funding source

BCSFA (BC Salmon Farmers Association) + Ocean AID + DFO (Department of Fisheries and Oceans). Mixed federal + industry + Indigenous funding. No single federal programme funds Salmon Vision. This is the framework-deployment funding-instrument gap pattern (G-313).

Operational status

Operational at Bear River + Kitwanga River (Gitanyow); expanded to Koeye River (Heiltsuk) October 2023. Pilot 2023 → broader deployment 2024+ ongoing. Goal stated as “real-time count data by 2024” (Atlas per EurekAlert 28 Sep 2023). Forward-looking: stated expansion to a half-dozen new watersheds on B.C.’s North and Central Coast (per Atlas, EurekAlert). G-336.

Framework citation

Salmon Vision does not formally cite OCAP®/CARE/NISR/IEEE 2890-2025 in its published documentation. The deployment integrates Traditional Knowledge with AI but does not formally name a framework as governing document. Same framework-deployment operationalisation pattern as PolArctic — alignment implicit, not explicit.

Why this matters for talks

For archetype 05 (critical-lens Indigenous sovereignty): Salmon Vision is the substantive operational case for Indigenous-led AI in BC. The talk-ready claim: “Salmon Vision is what data sovereignty by design looks like — Indigenous-led methodology (fish weir + Gitanyow knowledge) integrated with cutting-edge AI (computer vision, 500,000+ frames annotated). The deployment is peer-reviewed, operational, and expanding to new rivers.”

For archetype 02 (data-sovereignty Canadian): Salmon Vision is data-sovereignty-by-design — community-stewarded, peer-reviewed, anchored in centuries-old Indigenous methodology. The talk-ready claim: “Salmon Vision’s 90% coho detection accuracy and Frontiers in Marine Science 2023 peer review put Indigenous-led wild salmon AI on par with vendor-deployed aquaculture AI — and the Indigenous-led deployment outperforms on community accountability.”

For archetype 06 (regional-cluster-comparison): Salmon Vision anchors the BC Central Coast Indigenous-led fisheries AI cluster, distinct from the EU cooperative cluster model and the US land-grant-extension cluster model.

Substantive contested claims surfaced

Substantive gaps surfaced

Freshness and source provenance