SBIR Phase I: High-Resolution Image Segmentation for Natural Resource Management

SBIR 第一阶段：用于自然资源管理的高分辨率图像分割

• 批准号: 2112419
• 负责人: Ross Davison
• 金额: $ 25.6万
• 依托单位: COMON SOLUTIONS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112419&HistoricalAwards=false
• 关键词: SBIR; Phase; Resolution; Image; Segmentation

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to produce currently unavailable high-resolution vegetation maps and analyses that enable stakeholders (i.e., government agencies, academic researchers, land managers, non-governmental organizations, and private companies) to rapidly assess the health of ecosystems that are threatened by human development and environmental change. Producing this information will result in better management of natural lands and their associated services and goods, globally valued at $125 trillion, such as buffering current and future infrastructure from natural disasters (i.e., managing wetlands that dampen storm surge) and improving human health and well being outcomes (i.e., disease prevention and livelihood security, respectively). Compared to traditional ground surveying methods, this project will revolutionize the ecosystem health evaluation and management process by reducing work hours by approximately 50-90% and project costs by approximately 40%-70%. This SBIR Phase I project will demonstrate the feasibility to expand the accessibility and scalability of machine learning image segmentation to the fields of natural resource management, environmental conservation, and ecological research. The technical innovation of this project is a replicable machine learning model for vegetation analysis and ecosystem assessment that will expedite the ability to process and classify aerial imagery into individual species layers that can be used to assess vegetation composition and dynamic changes in species populations most influenced by human activity and climate change on a global scale. While there are examples of employing machine learning image segmentation in these fields, they are specific to regions or species and are incapable of scaling across diverse ecosystems and image resolution levels. The goal of the project is to create a machine learning model that can quickly and accurately delineate vegetation types from aerial imagery across diverse sets of data. This goal will be achieved following a development strategy of model exploration, data collection/annotation, model refinement, testing and evaluation, and model deployment.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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是制作目前无法获得的高分辨率植被地图和分析，使利益相关者（即，政府机构、学术研究人员、土地管理者、非政府组织和私营公司），以迅速评估受到人类发展和环境变化威胁的生态系统的健康状况。产生这些信息将导致更好地管理自然土地及其相关的服务和商品，全球价值125万亿美元，例如缓冲当前和未来的基础设施免受自然灾害的影响（即，管理抑制风暴潮的湿地）和改善人类健康和福祉结果（即，疾病预防和生计保障）。与传统的地面测量方法相比，该项目将彻底改变生态系统健康评估和管理过程，减少约50-90%的工作时间和约40%-70%的项目成本。SBIR第一阶段项目将展示将机器学习图像分割的可访问性和可扩展性扩展到自然资源管理，环境保护和生态研究领域的可行性。该项目的技术创新是一个用于植被分析和生态系统评估的可复制机器学习模型，这将加快处理航空图像并将其分类为单个物种层的能力，这些层可用于评估全球范围内受人类活动和气候变化影响最大的物种种群的植被组成和动态变化。虽然在这些领域中有使用机器学习图像分割的例子，但它们特定于区域或物种，并且无法在不同的生态系统和图像分辨率水平上扩展。该项目的目标是创建一个机器学习模型，可以快速准确地从不同数据集的航空图像中描绘植被类型。这一目标将通过模型探索、数据收集/注释、模型改进、测试和评估以及模型部署的发展战略来实现。该奖项反映了NSF的法定使命，并被认为值得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来支持。