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

SBIR 第一阶段：用户生成的实时定性数据处理，用于气候影响模型验证、集成和增强

• 批准号: 2216888
• 负责人: Julia Drapkin
• 金额: $ 27.44万
• 依托单位: ISEECHANGE, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2216888&HistoricalAwards=false
• 关键词: SBIR; Phase; User; generated; real

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.

该SBIR I期项目的更广泛影响是开发一种综合方法，以利用居民对洪水（和其他气候变化）事件的经验来实时验证建模，信息政策并提供设计基础设施开发。它提供了一种集成解决方案，用于捕获人们的直接经验（照片，故事和数据）捕获的有价值的信息，而气候变化却没有得到充分利用。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的混合，并将其处理为可用于理解现场影响，洪水发生和严重性的格式，并将这些数据交付给计划者和建模者，以开发更好地管理地板的方法。数据处理发生在幕后，并使居民能够以新的方式参与影响其社区的计划过程，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的研究，在公民数据系统中产生新的工作，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的通信和建立信任。数据科学的进步允许分析异质性定性和图像数据，将用户生成的帖子纳入气候弹性围绕大规模的基础架构计划。当前，用户提交的描述性数据和照片已手动分析内容。通过新的自然语言处理（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开发应用程序编程接口（API）来策划水文模型开发人员的数据，该项目可以自动提取洪水事件的社区所产生的全部价值。成功后，超本地用户生成的帖子将进行实时处理，以向计划者，模型验证以及社区成员本身提供有关洪水事件的详细数据。该产品构建了创新的技术，以便进行大规模处理，以便任何社区体验洪水事件都可以产生实时洪水数据并监测基础设施的影响，因为氢化基准继续发生变化。 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 precious of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.