SBIR Phase II: Arctic Environmental Modeling with Augmentation and Curation from an Artificial Intelligence Engine

小企业创新研究（SBIR）第二阶段项目的更广泛影响将满足对北极精确模型和预测日益增长的需求。 随着北极海洋作业（如捕鱼、航运和海带养殖）越来越多地受到前所未有的气候变化的影响，传统的建模技术无法支持这些新的需求。生态系统不是静止的，其不可预测性阻碍了社区的安全和可持续经济发展。这种建模方法通过增加短期（精确捕鱼）和长期（生态系统建模）资源的透明度来支持阿拉斯加渔业的经济竞争力。研究结果可用于确定新的和正在出现的渔业地点、捕捞不足或过度捕捞地点，以及可以恢复的地点与生态系统已不再支持的地点之间的差异。通过应用动态生态信息，该项目可以提供改善海洋资源管理的工具，从而在增加行业利润的同时提高可收获的总生物量。小企业创新研究（SBIR）第二阶段的创新是创建和完善能够生成定制环境模型的人工智能（AI）引擎，使需要解决方案的人们和社区能够快速获得新的和新兴的科学。人工智能生产的软件将整合近岸水深测量模型，栖息地测绘，精确捕鱼和基于生态系统的渔业管理（EBFM）工具等领域的多种科学技术。扩展定制模型的生成为海洋环境挑战提供了具有成本效益、适应性强和准确的解决方案。成功执行这一计划将需要技术和计算技能的结合;数值建模专业知识;遥感，测深，海冰，物理海洋学和渔业科学的重要科学素养;以及新型神经网络架构的开发和培训。 在第一阶段的工作中，来自卫星的遥感被用来在原型AI引擎中绘制近岸地图。利用新的卫星资源进行遥感使这项工作能够更深入地了解北冰洋等世界偏远地区。这些数据沙漠可以利用卫星和土著传统知识的口袋来建立富有活力和适应性强的生产性新经济。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase II project will address the growing need for accurate models and forecasts of the Arctic. As Arctic maritime operations such as fishing, shipping, and mariculture of kelp are increasingly impacted by unprecedented climate change, traditional modeling techniques are unable to support these new demands. Ecosystems are not static, and their unpredictability hinders safe and sustainable economic development for communities. This modeling approach supports the economic competitiveness for Alaskan fisheries by increasing transparency of resources on short timescales (precision fishing) and long timescales (ecosystem modeling). The results could be used to identify new and emerging locations for fisheries, under- or over-fished locations, and differences between locations that can be restored or those that the ecosystem has shifted away from supporting. By applying dynamic ecology information, this project can provide tools to improve management of ocean resources, which could increase industry profits while simultaneously raising the total harvestable biomass.The Small Business Innovation Research (SBIR) Phase II innovation is to create and refine an Artificial Intelligence (AI) engine capable of generating custom environmental models, making new and emerging science quickly accessible to the people and communities that need the solutions. The AI-produced software will integrate multiple types of scientific techniques from the fields of nearshore bathymetry models, habitat mapping, precision fishing, and Ecosystem Based Fisheries Management (EBFM) tools. Scaling the generation of tailored models provides cost effective, adaptable, and accurate solutions to ocean environmental challenges. Successfully executing this plan will require a combination of technical and computing skills; numerical modeling expertise; significant scientific literacy in remote sensing, bathymetry, sea ice, physical oceanography, and fisheries science; and the development and training of novel neural network architectures. In Phase I work, remote sensing from satellites was used to map the nearshore in the prototype AI engine. Remote sensing from new satellite resources has enabled this effort of a deeper understanding of the world’s remote locations, like the Arctic Ocean. These data deserts can leverage satellites and pockets of Indigenous Traditional Knowledge to build productive, new economies that are resilient and adaptable.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.