# Efficient Endangered Deer Species Monitoring with UAV Aerial Imagery and Deep Learning

arXiv: https://arxiv.org/abs/2506.00164 · HTML: https://mlsanity.com/paper/2506.00164/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 2 of 2)

- [Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV Images](https://mlsanity.com/paper/2108.07582/) (2021)
- [Automated Aerial Animal Detection When Spatial Resolution Conditions Are Varied](https://mlsanity.com/paper/2110.01329/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Evaluation of YOLO-based Deer Detection Performance on Edge Devices](https://mlsanity.com/paper/2509.20318/) (2025)
- [Weakly supervised framework for wildlife detection and counting in challenging Arctic environments: a case study on caribou (Rangifer tarandus)](https://mlsanity.com/paper/2601.18891/) (2026)

## Abstract (arXiv)

This paper examines the use of Unmanned Aerial Vehicles (UAVs) and deep learning for detecting endangered deer species in their natural habitats. As traditional identification processes require trained manual labor that can be costly in resources and time, there is a need for more efficient solutions. Leveraging high-resolution aerial imagery, advanced computer vision techniques are applied to automate the identification process of deer across two distinct projects in Buenos Aires, Argentina. The first project, Pantano Project, involves the marsh deer in the Paran'a Delta, while the second, WiMoBo, focuses on the Pampas deer in Campos del Tuy'u National Park. A tailored algorithm was developed using the YOLO framework, trained on extensive datasets compiled from UAV-captured images. The findings demonstrate that the algorithm effectively identifies marsh deer with a high degree of accuracy and provides initial insights into its applicability to Pampas deer, albeit with noted limitations. This study not only supports ongoing conservation efforts but also highlights the potential of integrating AI with UAV technology to enhance wildlife monitoring and management practices.