Innovative Digital Citizen Science: Active Learning for Disaster Relief

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Citizen science has been used for a wide variety of scientific discoveries, many of which have been enabled by the technical framework and expertise of the Zooniverse citizen science platform. The project team has been involved in applying the algorithms developed to make optimal use of inputs from citizen science volunteers in disciplines ranging from astronomy to zoology; among these are projects where the crowd assesses satellite and aerial imagery following natural disasters in order to rapidly provide responders and decision-makers with accurate situational information across wide geographical areas.This project seeks to advance this work by building on a recent STFC-supported project to develop novel integrations of citizen science classifications and machine learning algorithms. Specifically, the project will develop and test an "Active Learning" system where the machine and citizen scientists collaborate in real time to provide the most accurate assessments of infrastructure damage and signs of life, as quickly as possible. The machine is first trained using the results of previous humanitarian crowdsourcing deployments, and then during a live deployment asks the crowd for further information where it deems this to be of the most use in providing high-quality data to responders and decision-makers on the ground in affected areas.This system will be of obvious and immediate use for future disaster relief deployments. Additionally, the nature of the well-developed Zooniverse platform means that the software developed here can be applied to other disciplines where researchers need to be able to accurately detect changes in data. These include astrophysics, where researchers examine the changing sky for exploding stars and transiting exoplanets, and ecology and conservation, where researchers actively study animal migration and detect human activities such as poaching.