Deep Learning for Environmental Monitoring and Conservation: Applications, Approaches, Challenges, and Future Perspectives

This work provides a systematic review of deep learning methods applied to environmental monitoring and conservation. We follow the PRISMA guidelines for literature identification, screening, and selection, resulting in 104 studies included in the analysis. The review summarizes the main application areas, data modalities, architectural choices, and examines the methodological patterns and challenges that shape how deep learning is used in this domain. The synthesis highlights the capacity of deep learning models to handle complex spatial information and heterogeneous environmental data, while also revealing recurring limitations related to data representativeness, evaluation consistency, and limited operational validation. These observations point to the need for future research that advances data diversity, ecologically aligned evaluation protocols, real-world testing, and sustainable computational practices, enabling deep learning to make more reliable and impactful contributions to environmental conservation.