ABSTRACT
Oil spills pose a significant threat to the environment and human health, and the cleanup process is often challenging, time-consuming, and costly. Utilizing innovative solutions, such as crowdsourcing, can significantly improve response and mitigation efforts. Crowdsourcing involves the collection and analysis of data from a large number of individuals. Its use is becoming increasingly common in natural disasters to quickly locate survivors and to effectively identify and prioritize damaged areas. In the case of oil spills, crowdsourcing can be used to pinpoint the source of a spill, identify oiled wildlife, or track oil slicks. Recent incidents that have used crowdsourcing include Deepwater Horizon (2010), Hurricane Sandy (2012), and Hurricane Matthew (2016). However, there are limitations to crowdsourcing, such as the reliability and accuracy of user-generated data. The four crowdsourcing methods discussed in this paper are online participatory mapping, satellite image analysis, social media analysis, and requests for knowledge. Each method has unique strengths and limitations, requiring careful consideration for effective implementation. This paper examines each method's viability in an oil spill event by presenting case studies, challenges, and considerations for oil spill planning, response, and restoration. It concludes with recommendations for the use of each method and emphasizes the importance of combining diverse crowdsourced data for a comprehensive understanding of oil spill events.