Monitoring protected areas by integrating machine learning, remote sensing and citizen science

Abstract Protected Areas (PAs) are central to addressing the world's biodiversity crisis, but their effectiveness for conservation varies. Therefore, high‐resolution habitat condition monitoring is needed to evaluate their individual impacts. Critically, monitoring must efficiently scale to cover large areas and be conducted at regular intervals. Remote sensing (RS) data and citizen‐science (CS) species data are two sources of global data available for habitat condition monitoring, and integrating these could provide high‐resolution, scalable biodiversity data required for the detailed monitoring of PAs. However, integrating these presents four data analysis challenges: RS data are large and complex, large‐scale CS data are biased, integrating RS and CS data is non‐trivial, and fine‐tuning to local priorities is required. Machine Learning (ML) methods can address these challenges: geospatial foundation models for RS data can compress large data volumes, ML de‐biasing techniques can improve CS data quality, deep learning and multimodal ML can help to integrate RS and CS data, and transfer learning can fine‐tune models to local priorities. Here, we review these techniques and discuss how they can be applied to habitat condition monitoring. Practical implication . Together, these advances in ML can deliver high‐resolution biodiversity data that can be tailored to local priorities, enabling the efficient monitoring of PAs at scale, with the potential to support spatial land use decision‐making.