Evaluating machine learning models for multi-species wildlife detection and identification on remote sensed nadir imagery in South African savanna

This research paper investigates the efficacy of leading machine learning (ML) models for detecting and identifying ungulate species in the African savanna using nadir imagery from unmanned aerial vehicles (UAVs). Traditional aerial counting methods, while widely used, suffer from significant limitations in accuracy and precision, in part due to human biases. We examine the use of ML and its potential for aerial censuses by evaluating the performance of nine leading ML models, focusing on their ability to detect and identify five ungulate species: impala (Aepyceros melampus), nyala (Tragelaphus angasii), sable (Hippotragus niger), roan (Hippotragus equinus), and buffalo (Syncerus caffer). Using a UAV, 20137 nadir images were obtained from two properties in north-east South Africa. Data were manually annotated using bounding boxes and split into training, validation and test sets. ML models were trained on the same sets and run for the detection of wildlife as a single class and for iden..., , # Data from: Evaluating machine learning models for multi-species wildlife detection and identification on remotely sensed nadir imagery in the South African savanna

Dataset DOI: [10.5061/dryad.9ghx3ffvc](https://doi.org/10.5061/dryad.9ghx3ffvc)

## Description of the data and file structure

Nadir imagery was obtained using a DJI Matrice 300 RTK UAV with a DJI Zenmuse P1 45MP RGB camera. Flight paths were preprogrammed, with 70% front overlap and 53% side overlap, and 11 m/s flying at 180 m above ground level, resulting in a ground sampling distance (GSD) of 2.3 cm. All camps were flown three times during the dry season (2–5 October 2023) and three times during the wet season (4–6 February 2024). In addition, several extra flights were conducted for algorithm training. These files can be identified by the word extradite in their filenames. The additional files are automatically generated by the drone and contain information on the actual flight specifications and flight paths.

### Fi...,