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

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响/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）能够快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。提供这些信息将有助于更好地管理全球价值125万亿美元的自然土地及其相关服务和货物，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），以及改善人类健康和福祉结果（即分别预防疾病和保障生计）。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，减少约40%-70%的项目成本。这个SBIR一期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用于植被分析和生态系统评估的可复制机器学习模型，该模型将加快处理航空图像并将其分类为单个物种层的能力，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，但它们是特定于区域或物种的，无法跨越不同的生态系统和图像分辨率水平进行缩放。该项目的目标是创建一个机器学习模型，该模型可以从不同数据集的航空图像中快速准确地描绘植被类型。这一目标将遵循模型探索、数据收集/注释、模型精化、测试和评估以及模型部署的开发策略来实现。该奖项反映了美国国家科学基金会的法定使命，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/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.