Citizen Science EAGER: Quantifying Uncertainty in Crowd Response for Reliable Wind Hazard and Damage Assessment

公民科学 EAGER：量化人群反应的不确定性，以进行可靠的风灾和损害评估

• 批准号: 1645386
• 负责人: Hadi Meidani
• 金额: $ 10万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1645386&HistoricalAwards=false
• 关键词: Citizen; Science; EAGER; Quantifying; Uncertainty

Damage to infrastructure arising from windstorms exceeds damage from any other natural hazard in the U.S. The highly variable nature of wind loadings on buildings during a windstorm, however, means that accurate characterization at the damage location may not be captured by current measurement networks. Ubiquitous smartphone and internet availability, widespread use and rapid dissemination of social media, the power of crowds engaged in scientific endeavors, and the public's awareness of vulnerabilities point to a paradigm shift in sensing hazards in general. In the case of windstorm damage, on-the-ground data retrieved and shared by Citizen Science public participation may provide windstorm data previously unavailable. The primary focus of this EArly-concept Grant for Exploratory Research (EAGER) award is to study "human-sensor" data collected through Amazon Mechanical Turk-- a crowd sourcing application. Volunteers will be shown images and related data from actual windstorms and asked to characterize the damage and their confidence in their assessments. These data will be used to design a crowd sourcing algorithm that will enable robust Citizen Science public participation in the rapid identification of damage areas to help decision makers to allocate resources for damage response and recovery efforts and for targeted damage assessments, which can help to improve the design of buildings in regions susceptible to intense windstorms. This project will address two key questions: How can one quantify the confidence in crowdsourced damage assessment? How can one design a tool for more reliable crowdsourcing given unreliable participants? Researchers will initially compile a "validation set" of data that includes imagery, damage states and wind speed estimates for approximately 8,000 structures that were affected by the Joplin, MO tornado. The data set will be used to create a reliable crowdsourced image classification scheme in the form of online questionnaires for the public. The questionnaires will be tested by collecting assessment reports from participants on Amazon Mechanical Turk. These reports will inform development of an uncertainty model for participant reliability, which forms the basis for a coding-theoretic crowdsourcing algorithm that is robust to uncertainties due to unreliable participants. This algorithm will be tested against a separate dataset to compare the researchers' approach with one that doesn't control for participant unreliability. The final research results will be shared with NOAA for use in training surveyors to assess wind damage and to provide tutorials for the public.

在美国，风暴对基础设施造成的损害超过了任何其他自然灾害造成的损害。然而，风暴期间建筑物上风荷载的高度可变性意味着当前测量网络可能无法捕获损坏位置的准确特征。无处不在的智能手机和互联网可用性，社交媒体的广泛使用和快速传播，参与科学努力的人群的力量，以及公众对脆弱性的认识，都指向了感知危险的范式转变。 在风暴破坏的情况下，由公民科学公众参与检索和共享的地面数据可能会提供以前无法获得的风暴数据。EARLY概念探索性研究补助金（EAGER）的主要重点是研究通过亚马逊Mechanical Turk（一种众包应用程序）收集的“人类传感器”数据。 志愿者将看到来自实际风暴的图像和相关数据，并被要求描述损失及其对评估的信心。 这些数据将用于设计一个众包算法，使公民科学公众能够积极参与快速识别受损地区，帮助决策者分配资源用于损害应对和恢复工作以及有针对性的损害评估，这有助于改善易受强烈风暴影响地区的建筑物设计。 该项目将解决两个关键问题：如何量化众包损失评估的信心？ 如何设计一个工具，让不可靠的参与者更可靠的众包？研究人员将首先汇编一组“验证数据”，其中包括受密苏里州乔普林龙卷风影响的大约8,000个结构的图像、损坏状态和风速估计。该数据集将用于以在线问卷的形式为公众创建一个可靠的众包图像分类方案。调查问卷将通过收集Amazon Mechanical Turk参与者的评估报告进行测试。这些报告将为参与者可靠性的不确定性模型的开发提供信息，该模型构成了编码理论众包算法的基础，该算法对不可靠参与者的不确定性具有鲁棒性。该算法将针对一个单独的数据集进行测试，以比较研究人员的方法与不控制参与者不可靠性的方法。最终的研究结果将与NOAA分享，用于培训调查员评估风灾，并为公众提供教程。