A Crowdsourced Knowledge Base for the Damage Assessment of Extreme Events

使用众包志愿者分析遥感图像是一种相对较新的损害评估方法，是在2008年四川地震后开发的，并在2010年海地和2011年新西兰地震期间正式化。Web 2.0技术的出现以及具有高空间、光谱和时间分辨率的天气遥感图像的普遍存在，使这种方法成为可能。所证明的好处是在提供损失估计方面加快了两到三倍。然而，这种用于损害评估的人工众包方法的成功取决于人群的规模和可靠性。这项研究将侧重于一个新的框架，称为极端事件的众包知识库（Building A Crowdsourced Knowledge Base of Extreme Events），利用遥感图像进行极端事件损害评估，自动发现和分类损害，并建立一个数据驱动的损害特征知识库，可在未来的事件中重复使用。CNOBONE是对人工众包方法进行的一次改造，在灾害社区中是前所未有的，它将众包的力量与计算机科学和图像处理的最先进方法相结合。它用基于对象的变化检测和分类的自动化方法取代了手动工作，提高了速度，降低了损失评估的成本，并很好地扩展到数据量的增加。它通过众包主动学习将人群从手动注释的任务转移到对自动化方法性能的质量保证反馈。这提高了评估的准确性，同时将框架的成功与人群的规模和可靠性脱钩，因为反馈是从评分良好的注释者那里征求的，并且只在困难的情况下。 它还整合了多种遥感产品，并进行数据融合，将其输出统一为一个共同的损害地图。 随着数据量和产品的激增，这是下一代损伤评估的必备特性。使用各种数据产品，特别是来自高空间分辨率和对天气和太阳光照不太敏感的不可见波段的图像，将更好地区分某些损害类型，研究的更广泛影响是减少极端事件的总体人力和财政成本，为灾后需求评估提供快速准确估计损害的新方法。知识库的管理可以在灾害发生时迅速建立有效的模型，在评估脆弱性的事件模拟中完善损害预测，并促进更好的土地使用规划，鼓励发展具有抗灾能力的社区。这项工作还包括一个基于网络的损害评估模拟器，该模拟器绘制最近地震事件的遥感地震图像，以使更多的公众参与减灾。此外，研究人员将积极招募来自代表性不足群体的研究生和本科生，并在多学科合作中指导他们。机器学习学生将学习遥感和损害评估，地球工程学生将学习机器学习和统计数据分析的基础知识。

The use of crowdsourced volunteers to analyze remote sensing imagery is a relatively new damage assessment approach, developed in the wake of the 2008 Sichuan earthquake, and formalized during the 2010 Haiti and 2011 New Zealand earthquakes. This approach is enabled by the advent of Web 2.0 technologies and the ubiquity of free remote sensing images that are synoptic with high spatial-, spectral- and temporal-resolutions. The demonstrated benefit was a speedup by a factor of two or three in the delivery of damage estimates. However, the success of this manual crowdsourced approach for damage assessment is dependent upon the size and reliability of the crowd. This research will focus on a new framework called BACKBOnE (Building A Crowdsourced Knowledge Base of Extreme Events) for extreme event damage assessment utilizing remotely sensed images that automatically finds and classifies damages, and builds a data-driven knowledge base of damage characteristics that can be reused during future events. BACKBOnE is a transformation of the manual crowdsourced approach for damage assessment and is unprecedented in the disasters community, combining the power of crowdsourcing with state-of-the-art methods from computer science and image processing. It replaces the manual effort with automated methods for object-based change detection and classification that increase the speed, reduce the cost of damage assessment, and scale well to increases in data volume. It shifts the crowd from its task of manual annotation to quality assurance feedback on the performance of automated methods via crowdsourced active learning. This improves assessment accuracy, while decoupling the framework's success from the size and reliability of the crowd because feedback is solicited from annotators scored favorably, and only on difficult cases. It also incorporates a multitude of remote sensing products and performs data fusion to unify their outputs into a common map of damage. This is a must-have characteristic of next-generation damage assessment as data volumes and products proliferate. The use of diverse data products, particularly imagery from high spatial resolutions and non-visible bands that are less sensitive to weather and solar illumination, will better discriminate certain damage types.The broader impact of the research is the reduction of the overall human and financial cost of extreme events by contributing new methods for rapid and accurate damage estimates used for Post-Disaster Needs Assessment (PDNA). The curation of a knowledge base builds effective models quickly when a disaster strikes, refines damage predictions in event simulations that assess vulnerability, and fosters better land-use planning that encourages the growth of disaster resilient communities. The work also includes a web-based damage assessment simulator that maps remotely sensed earthquake images from recent earthquake events to engage the greater public in disaster mitigation. In addition, the investigators will actively recruit graduate and undergraduate students from under-represented groups and mentor them within a multi-disciplinary collaboration. Machine learning students will learn about remote sensing and damage assessment, and geoengineering students will learn fundamentals of machine learning and statistical data analysis.