How can crowdsourcing analysis of drone footage be sustained after a crisis has passed?
This experiment will explore how to sustain people's engagement in crowdsourcing analysis for relief efforts after the immediate emergency has passed. The first part of the experiment will test strategies such as task variation and sequencing to sustain crowdsourcing of drone footage analysis in post-disaster and development situations. The second part looks into how teams of experts and volunteers learn over time and acquire skills and motivation to approach more challenging image analysis tasks.
New technologies like drones or social networks are routinely used during large-scale emergencies. For example, in natural disasters, drones can capture aerial images of disaster-affected communities in much higher resolution, more quickly, and at much lower costs than helicopters or satellites. The content generated by drones needs to be analysed, often under time pressure, to enable aid workers to coordinate recovery efforts ground effectively. Thanks to mobile technologies such as smartphones, the analysis can be crowdsourced from citizens all over the world. During an emergency, high media coverage attracts a lot of volunteers to analyse drone footage, but the number of participants decreases once the media attention fades. This can have problematic consequences for long-term recovery, as reduced participation in crowdsourcing efforts means it gets harder to keep maps and information up to date and to allocate resources in an effective way.
These findings will help build understanding of the tactics that could sustain and improve citizen contributions over a longer time period. The experiment’s outcomes will be relevant for humanitarian relief efforts, citizen science and all crowdsourcing projects that struggle to maintain engagement.