Data Fusion to Describe and Quantify Search and Rescue Operations in the Mediterranean Sea

Abstract

The Mediterranean Sea is the stage of one of the biggest humanitarian crises to affect Europe. Since 2014, thousands of migrants and refugees have died or gone missing in dangerous attempts to cross into the continent. However, there is relatively little structured information available on how they attempt the crossing. Such information could be used to better target maritime rescue efforts or to anticipate smuggling patterns, which could potentially save lives. In this article, we provide an overview of data sources available for the study of migration in the Central Mediterranean. We describe how these data can be structured, combined, and analyzed to provide quantitative insights on the situation in the region. We define a quantified rescue framework for fusing different data sources around individual rescue operations, and we explore the potential of machine learning to perform automated rescue detection based on vessel trajectory information. We conclude with technical research questions, and potential policy and operational implications related to the use of these data sources.