AI Insight
This study evaluates how well machine learning models trained to identify African wildlife species from camera trap images in the Serengeti perform when applied to different African regions. Researchers compared three approaches: a supervised model fine-tuned on Serengeti data, a retrieval-based model using Serengeti as reference, and a zero-shot foundation model requiring no Serengeti training data. All models were tested on camera trap datasets from Southern African locations including Kgalagadi, Kruger National Park, and Botswana to assess performance degradation across geographic domains.
Why it matters
This research addresses a critical practical challenge for conservation efforts: deploying automated wildlife monitoring systems across different African ecosystems without needing to collect and label new training data for each location. The findings provide guidance for choosing appropriate AI approaches when extending camera trap classification systems to new regions with limited labeled datasets.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Camera trap networks such as Snapshot Safari have generated millions of labelled wildlife images across Africa, enabling the training of deep learning models for automated species classification. However, deploying models trained in one African region to another remains poorly understood. To the best of our knowledge, this study presents the first systematic evaluation of geographic domain shift within the African continent for wildlife camera trap species classification, using the Machine Learning sub-field of Artificial Intelligence. We use three model architectures, each interacting with Snapshot Serengeti in a different way: BEiTV2is fine-tuned on Serengeti images as a supervised baseline; DINOv2 with FAISS uses Serengeti images as a retrieval index without any weight updates; and BioCLIP is a true zero-shot foundation model that receives no Serengeti training data at all. All three are then evaluated on two Southern African test sets, Snapshot Kgalagadi and Snapshot Kruger, as well as on locally collected wildlife photographs from Botswana. We conduct eight experiments covering in-domain baselines, cross-dataset transfer, data scaling, MegaDetector preprocessing, grayscale vs. colour image conditions, and per-species transfer analysis. This work provides the first empirical characterisation of intra-African domain shift across both supervised and zero-shot architectures, and offers practical guidance for conservation AI practitioners who need to deploy models across the diverse ecosystems of Southern Africa without collecting new labelled data.