SBIR Phase I: User-generated real time qualitative data processing for climate impacted model validation, integration, and augmentation

SBIR一期项目的更广泛影响是开发了一种综合方法，利用居民对洪水（和其他气候变化）事件的经验来验证实时建模，为政策提供信息，并为基础设施发展提供设计见解。它提供了一种集成的解决方案，用于捕获人们对气候变化的直接体验（照片、故事和数据）所捕获的有价值的信息，否则这些信息将得不到充分利用。该团队将开发一个社区知识平台，该平台可以处理居民提交的文本和照片数据，并将其处理成可用于了解地面影响、洪水发生情况和严重程度的格式，并将这些数据提供给规划者和建模者，以制定更好地管理洪水的方法。数据处理在幕后进行，使居民能够以新的方式参与影响其社区的规划过程，增加代表性不足的社区的机会，并提高决策的公平性。该项目将刺激数据科学领域的研究，在公民数据系统领域创造新型就业机会，并提高公共基础设施投资的效率。用户的手机将成为强大的本地数据收集工具，允许在政府决策者、科学家和居民之间建立直接联系和信任。数据科学的进步允许对异构定性和图像数据进行分析，将用户生成的帖子纳入围绕气候适应能力的大规模基础设施规划中。目前，用户提交的描述性数据和照片是手工分析内容的。通过新颖地使用自然语言处理（NLP）、空间数据分析、人工智能（AI）和洪水事件照片的计算机视觉，以及为水文模型开发人员开发应用程序编程接口（API）来管理数据，该项目自动化了提取社区生成的洪水事件帖子全部价值的过程。如果成功，hyperlocal用户生成的帖子将被实时处理，向规划人员提供详细的洪水事件现场数据，用于模型验证，以及社区成员自己。该产品建立了创新技术，允许大规模处理，以便任何经历洪水事件的社区都可以生成实时洪水数据，并在水文基线不断变化时监测基础设施的影响。该项目开发了新的机器学习NLP，用于自动分析定性文本数据，用于情感分析和影响的关键字检测，用于从照片中提取洪水特征的AI，以及用于保护模型IP的API，同时允许与外部数据集成以进行验证。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 of this SBIR Phase I project is the development of an integrated methodology to use resident’s experiences about flood (and other climate change) events to validate modeling in real time, inform policy, and provide design insights for infrastructure development. It provides an integrated solution for capturing the valuable information captured by people’s direct experiences (photos, stories, and data) with climate change that are otherwise underutilized. The team will develop a community knowledge platform that can process a mix of text and photo data submitted by residents and process it into formats usable for understanding on-the-ground impacts, flood occurrence and severity, and deliver that data to planners and modelers developing ways to better manage floods. Data processing occurs behind the scenes and allows residents to engage with the planning processes impacting their communities in new ways, increase the access of underrepresented communities, and improve equity in decision-making. The project will stimulate research in data sciences, generate new types of jobs in civic data systems, and improve the efficiency of public infrastructure investments. User’s cell phones will become powerful local data collection tools allowing a direct line of communications and building trust between government decision makers, scientists, and residents. Advances in data science allows the analysis of heterogeneous qualitative and image data to incorporate user generated posts into large scale infrastructure planning around climate resilience. Currently, descriptive data and photos submitted by users are manually analyzed for content. Through novel use of natural language processing (NLP), spatial data analysis, artificial intelligent (AI) and computer vision of flood event photos, and development of an application programming interface (API) to curate data for hydrological model developers, this project automates the process of extracting the full value of community generated posts of flood events. When successful, hyperlocal user generated posts will be processed in real time to deliver detailed on-the-ground data on flood events to planners, for model validation, and community members themselves. The product builds innovative technologies to permit processing at scale so that any community experiencing flood events can generate real time flood data and monitor the impact of infrastructure as hydrological baselines continue to shift. The project develops new machine learning NLP to automate the analysis of qualitative text data, keyword detection for sentiment analysis and impact, AI to extract flood characteristics from photos, and API for protecting model IP while allowing integration with external data for validation purposes.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.