Crowdsourcing and Machine Learning for Disaster Relief and Resilience

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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.