EAGER: A Dynamic, Reliability-Weighted, Multi-Pass Probabilistic Framework to Reduce Uncertainty in Crowd-Sourced Post-Disaster Damage Assessments

EAGER：一种动态、可靠性加权、多通道概率框架，可减少众包灾后损失评估的不确定性

• 批准号: 1645335
• 负责人: Anne Kiremidjian
• 金额: $ 10万
• 依托单位: Stanford University
• 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: 2016-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1645335&HistoricalAwards=false
• 关键词: EAGER; Dynamic; Reliability; Weighted; Multi

A good estimate of the scale and spatial distribution of damage after a disaster is critical for effective response and recovery. It provides data to make best use of emergency responders, distribute medical, food or other aid, manage debris removal, and advocate for and leverage international aid based on a clear understanding of losses. Modern information technologies provide unprecedented data access to support damage assessment following a disaster, but these data are often very hard to assimilate, gauge its quality and coalesce into effective decision-support tools. In the past decade, crowdsourced and citizen-based analysis of satellite and aerial imagery have become additional mechanisms to analyze data from rapidly evolving disasters due to earthquakes, hurricanes and other hazards. Compared to standard data-gathering methods, these crowdsourcing initiatives have the advantage that the data obtained are typically much more numerous, have much larger spatial coverage and are obtained much more rapidly, but often have significant uncertainty. This has been a significant limitation to the widespread use of crowdsourced data and citizen science interpretation following disaster. This EArly-concept Grant for Exploratory Research (EAGER) project focuses on methodological improvements to crowdsourced post-disaster damage assessment as well as recovery monitoring. Several important impacts are expected, including the identification, measurement and systematic reduction in uncertainty in crowd-sourced damage assessments and the development of a probabilistic framework for quantifying and mapping post-disaster damage.The research marks a methodological paradigm shift in crowdsourced analysis of images, from the direct assignment of deterministic categorical damage states to the definition of full probability distributions of damage. This shift towards a probabilistic framework further enables the disaggregation of uncertainty into its various sources, each of which can be minimized individually. This is achieved by targeting improvements in the three components of the crowdsourced damage assessment process: set-up, imagery analysis, post-processing. Set-up: improving the crowdsourcing task definition and developing a standard visual damage taxonomy. Imagery analysis: improving the identification of damage indicators (e.g. less omissions and miss-categorizations) through multi-pass assessment. Post-processing: developing a weighted aggregation of identified damage indicators into probability distributions of damage. Results from this research are expected to support the development of effective decision-support tools used by emergency responders and disaster recovery planners, promoting more resilient communities in the United States and beyond.

准确估计灾后损失的规模和空间分布对于有效应对和恢复至关重要。它提供数据，以便最好地利用应急人员，分发医疗、食品或其他援助，管理碎片清除工作，并在明确了解损失的基础上倡导和利用国际援助。现代信息技术为灾后损失评估提供了前所未有的数据支持，但这些数据往往很难消化、衡量其质量并整合成有效的决策支持工具。在过去十年中，众包和基于公民的卫星和航空图像分析已成为分析地震、飓风和其他灾害造成的迅速演变的灾害数据的额外机制。与标准的数据收集方法相比，这些众包举措的优点是，所获得的数据通常数量多得多，空间覆盖面大得多，获得速度快得多，但往往具有很大的不确定性。这严重限制了灾害发生后广泛使用众包数据和公民科学解释。EARLY概念探索性研究补助金（EAGER）项目的重点是改进众包灾后损失评估和恢复监测的方法。预计将产生若干重要影响，包括确定、衡量和系统地减少众包损害评估的不确定性，以及制定一个灾后损害量化和绘图的概率框架，这项研究标志着众包图像分析方法的范式转变，从直接指定确定性的分类损害状态转变为定义损害的全概率分布。这种向概率框架的转变进一步使不确定性能够分解为各种来源，每一种来源都可以单独最小化。这是通过针对众包损害评估过程的三个组成部分进行改进来实现的：设置、图像分析和后处理。设置：改进众包任务定义并制定标准视觉损伤分类法。图像分析：通过多次评估改进损害指标的确定（例如减少遗漏和误分类）。后处理：将已确定的损害指标加权汇总成损害概率分布。预计这项研究的结果将支持开发应急响应人员和灾难恢复规划人员使用的有效决策支持工具，促进美国及其他地区更具复原力的社区。