用多模态AI和专家参与提升原住民流域鲑鱼管理效率
Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers
- 结合视频与声呐数据，用AI自动识别、计数和测长鲑鱼
- 专家验证+主动学习，减少标注负担并保证生态相关性
- 跨学科协作推动伦理AI与文化敏感型渔业管理
野生鲑鱼对北太平洋沿岸的生态、经济和文化可持续性至关重要。然而，气候变化、栖息地丧失以及缺乏基础设施的偏远生态系统带来的数据局限，给渔业管理带来巨大挑战。本项目探索将多模态基础AI与专家在环框架相结合，以增强太平洋西北地区原住民河流中野生鲑鱼的监测与可持续管理。通过视频与声呐监测，开发基于AI的自动化物种识别、计数与体长测量工具，显著降低人工成本，加快结果产出，提升决策准确性。专家验证与主动学习机制确保生态相关性的同时减轻标注负担。为应对独特的技术和社社会挑战，研究团队整合了大学研究人员、渔业生物学家、原住民保护实践者、政府机构及环保组织等多方力量。通过协同合作，推动伦理AI共同开发、开放数据共享及文化导向的渔业管理。
原文摘要 · Abstract (English)
Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.
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