Deep Learning for Combating Wildlife and Environmental Crime: A Survey

Falih Gozi Febrinanto, Sean P. McGowan, Jacob Maher, Phillip Cassey

Wildlife and Environmental Crime (WEC) describes illegal activities that threaten biodiversity and harm the environment. In recent years, deep learning (DL) has shown strong potential in combating WEC by learning complex patterns in data through the extraction of high-dimensional features. DL also offers powerful capabilities for prediction, classification, and object tracking, and has become an increasingly valuable tool in conservation efforts. This paper provides a comprehensive review of recent advances in DL implementations to combat WEC. We introduce a new taxonomy that identifies key intervention points across four contexts: field (observations in natural ecosystems); records (administrative and legal data); physical (specimens in laboratories, markets, and enforcement facilities); and online platforms. Moreover, we review existing DL-based applications for WEC prevention and discuss key opportunities, as well as the remaining challenges. Finally, we outline future research directions that may enable more robust DL-based approaches to combat WEC.