[en] As the need to accurately monitor key-species populations grows amid increasing pressures on global biodiversity, the counting of large mammals in savannas has traditionally relied on the Systematic-Reconnaissance-Flight (SRF) technique using light aircrafts and human observers. However, this method has limitations, including non-systematic human errors. In recent years, the Oblique-Camera-Count (OCC) approach developed in East Africa has utilized cameras to capture high-resolution imagery replicating aircraft observers’ oblique view. Whilst demonstrating that human observers have missed many animals, OCC relies on labor-intensive human interpretation of thousands of images. This study explores the potential of Deep Learning (DL) to reduce the interpretation workload associated with OCC surveys. Using oblique aerial imagery of 2.1 hectares footprint collected during an SRF-OCC survey of Queen Elizabeth Protected Area in Uganda, a DL model (HerdNet) was trained and evaluated to detect and count 12 wildlife and livestock mammal species. The model’s performance was assessed both at the animal instance-based and image-based levels, achieving accurate detection performance (F1 score of 85%) in positive images (i.e. containing animals) and reducing manual interpretation workload by 74% on a realistic dataset showing less than 10% of positive images. However, it struggled to differentiate visually related species and overestimated animal counts due to false positives generated by landscape items resembling animals. These challenges may be addressed through improved training and verification processes. The results highlight DL’s potential to semi-automate processing of aerial survey wildlife imagery, reducing manual interpretation burden. By incorporating DL models into existing counting standards, future surveys may increase sampling efforts, improve accuracy, and enhance aerial survey safety.
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture Global Conservation of California
Funding text :
The annotation work was supported under a grant from Global Conservation of California, USA to R. Lamprey and the WildSpace-Image-Analytics team of Uganda (www.wildspace-image-analytics.com). Sharing of annotation data was conducted under a Memorandum of Understanding between Uganda Conservation Foundation (UCF) and the University of Liege. The original aerial 2018 survey of QENP, from which this experimental imagery was collected, was funded by UCF with support from Global Conservation, Vulcan Inc., Save the Elephants and the Uganda Wildlife Authority. We are grateful to Jeff Morgan of Global Conservation and Mike Keigwin of the Uganda Conservation Foundation who supported R. Lamprey and the WildSpace-Image-Analytics team in Uganda in conducting the first-stage image annotation work of this project. We would like to thank the Uganda Wildlife Authority for their assistance in the original QEPA aerial survey of 2018, and especially to Mr Charles Tumwesigye, Director of Conservation at UWA, who kindly obtained the necessary authorizations.
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