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

对灾后破坏的规模和空间分布进行良好的估计，对于有效的应对和恢复至关重要。它提供数据，以便最大限度地利用应急人员，分发医疗、食品或其他援助，管理碎片清除，并在清楚了解损失的基础上倡导和利用国际援助。现代信息技术提供了前所未有的数据访问，以支持灾后损失评估，但这些数据往往很难吸收，很难衡量其质量，也很难整合成有效的决策支持工具。在过去十年中，对卫星和航空图像的众包和基于公民的分析已成为分析地震、飓风和其他灾害引起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，这些众包举措的优势在于，获得的数据通常要多得多，空间覆盖范围大得多，获得的速度也快得多，但往往具有重大的不确定性。这对灾后广泛使用众包数据和公民科学解释是一个重大限制。这一早期概念探索性研究赠款项目侧重于改进众包灾后损失评估和恢复监测的方法。预计将产生几个重要影响，包括确定、测量和系统减少众包损失评估中的不确定性，以及开发用于量化和绘制灾后损失图的概率框架。这项研究标志着众包图像分析的方法论范式发生了转变，从直接分配确定性的分类损害状态到定义损害的全概率分布。这种向概率框架的转变进一步允许将不确定性分解为各种来源，每个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而实现的：设置、图像分析、后处理。设置：改进众包任务定义，制定标准的视觉损伤分类。图像分析：通过多遍评估改进损害指标的识别(例如，减少遗漏和分类错误)。后处理：将已确定的损害指标加权汇总成损害的概率分布。这项研究的结果预计将支持开发应急人员和灾难恢复规划者使用的有效决策支持工具，促进美国和其他地区更具复原力的社区。

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.