Automated rhinoceros detection in satellite imagery using deep learning

Rhinoceroses face severe threats from poaching, habitat fragmentation, and ongoing habitat degradation. Monitoring rhinoceros across the vast, often inaccessible landscapes they inhabit is challenging. In this study, we assess the feasibility of detecting white rhinoceroses using very high-resolution (33-36 cm) satellite imagery acquired over the world's largest private rhinoceros reserve in South Africa using a YOLO-based object detection model (YOLOv12x). We test whether synthetic imagery enhances model performance, whether rhinoceroses can be reliably distinguished from elephants in satellite imagery, and whether synthetically generated rhinoceroses are visually distinguishable from real ones by human annotators. We achieve an average precision (AP) of 0.65 in detection accuracy with synthetic augmentation yielding a marginal improvement. This study provides a demonstration of monitoring rhinos using this approach and introduces an open-access dataset to support the development and testing of new models. The aim is to facilitate effective monitoring of rhinos across the vast landscapes they inhabit. Developing new detection techniques can strengthen conservation and recovery initiatives, including translocations, assessment of breeding program success, and evaluation of anti-poaching efforts.