Crowdsourcing and Machine Learning for Disaster Relief and Resilience

该项目建立在stfc支持的成功而有影响力的研究的悠久历史之上，在世界领先的Zooniverse公民科学平台内将这项研究应用于需要官方发展援助的国家的人道主义和灾害管理问题。行星响应网络是由Zooniverse、牛津大学机器学习小组以及响应和复原力慈善机构Rescue Global领导的合作伙伴关系。自2015年以来，PRN部署了众包项目，对尼泊尔、厄瓜多尔以及多米尼加和安提瓜和巴布达等多个加勒比国家发生重大自然灾害后的多种损害进行分类。该项目旨在通过纳入与“救援全球”合作的地面救援人员的反馈，以及最近一份多机构报告的反馈，在这些项目的成功基础上取得改进，该报告明确阐述了众包项目在人道主义应急应用中的独特需求。由于STFC的支持，Zooniverse拥有完善的平台基础设施，可以完全满足这些需求；本项目所要求的适度额外支持将为Zooniverse平台增加有针对性的高影响力功能，从而带来高性价比。这些功能包括将事件前和事件后的卫星图像快速处理为人群可分类的“主题”的管道，将stfc支持的机器学习研究应用于图像的预分类，将stfc支持的先进算法用于实时人机分类，以及对共识结果的直观可视化，以便决策者和地面反应人员可以轻松地解释损坏图并最大限度地提高态势感知。从而更好地分配资源和援助，更快地恢复基础设施，并对社会准备和从自然灾害中恢复产生重大的积极影响。

This project builds on a strong history of successful, impactful STFC-supported research, applying this research within the world-leading Zooniverse citizen science platform to humanitarian and disaster management issues in countries that require Official Development Assistance. The Planetary Response Network is a partnership led by the Zooniverse, the Machine Learning Group at the University of Oxford, and the response and resilience charity Rescue Global. Since 2015 the PRN has deployed crowdsourcing projects to classify multiple kinds of damage following major natural disasters in Nepal, Ecuador, and multiple Caribbean nations including Dominica and Antigua & Barbuda. This project seeks to improve on the successes of those projects by incorporating feedback from ground-responders partnered with Rescue Global and from a recent multi-agency report which clearly articulated the unique needs of crowdsourced projects in humanitarian response applications. Thanks to STFC support, the Zooniverse has well-established platform infrastructure that can fully address these needs; the modest additional support requested in this project will bring high value for money by adding targeted high-impact features to the Zooniverse platform. These features include a pipeline to rapidly process pre- and post-event satellite images into classifiable "subjects" for the crowd, application of STFC-supported machine learning research to pre-classification of images, incorporation of STFC-supported advanced algorithms for real-time human-machine classification, and intuitive visualisation of consensus results so that decision makers and responders on the ground can easily interpret damage maps and maximise situational awareness, leading to better allocation of resources and aid, faster restoration of infrastructure, and a significant positive impact on societies preparing for and recovering from natural disasters.