Data from: 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 identification of
each individual species. The models were compared across four metrics:
precision, recall, F1-score and mean average precision (mAP). The
resulting highest wildlife detection scores were: precision 86.7%, recall
81%, F1 82.6% and mAP 85%, with newer and smaller models generally
achieving higher scores than older and larger models respectively. Our
results show ML model’s animal detection rates comparable to highest human
detections during aerial censuses. However, species identification results
were overall lower with highest scores being: precision 59.4, recall 74.2,
F1-score 52.1% and mAP 55.7%, with significant variation between models
and species, influenced by body size, colour and dataset size. The lower
scores in species identification demonstrate that ML models are not yet
suitable for performing fully automated censuses. Incorporating ML in a
semi-automated process may however, achieve higher precision than using
human observers through the removal of human biases and greater
repeatability through the ability to pre-programme flight paths.