MMLA: Multi-Environment, Multi-Species, Low-Altitude Aerial Footage Dataset

• URL: http://arxiv.org/abs/2504.07744v1
• Date: Thu, 10 Apr 2025 13:40:27 GMT
• Title: MMLA: Multi-Environment, Multi-Species, Low-Altitude Aerial Footage Dataset
• Authors: Jenna Kline, Samuel Stevens, Guy Maalouf, Camille Rondeau Saint-Jean, Dat Nguyen Ngoc, Majid Mirmehdi, David Guerin, Tilo Burghardt, Elzbieta Pastucha, Blair Costelloe, Matthew Watson, Thomas Richardson, Ulrik Pagh Schultz Lundquist,
• Abstract summary: Real-time wildlife detection in drone imagery is critical for numerous applications, including animal ecology, conservation, and biodiversity monitoring.<n>We present a novel multi-species, multi-environment, low-altitude aerial footage (MMLA) dataset.<n>Results demonstrate significant performance disparities across locations and species-specific detection variations.
• Score: 3.7188931723069443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time wildlife detection in drone imagery is critical for numerous applications, including animal ecology, conservation, and biodiversity monitoring. Low-altitude drone missions are effective for collecting fine-grained animal movement and behavior data, particularly if missions are automated for increased speed and consistency. However, little work exists on evaluating computer vision models on low-altitude aerial imagery and generalizability across different species and settings. To fill this gap, we present a novel multi-environment, multi-species, low-altitude aerial footage (MMLA) dataset. MMLA consists of drone footage collected across three diverse environments: Ol Pejeta Conservancy and Mpala Research Centre in Kenya, and The Wilds Conservation Center in Ohio, which includes five species: Plains zebras, Grevy's zebras, giraffes, onagers, and African Painted Dogs. We comprehensively evaluate three YOLO models (YOLOv5m, YOLOv8m, and YOLOv11m) for detecting animals. Results demonstrate significant performance disparities across locations and species-specific detection variations. Our work highlights the importance of evaluating detection algorithms across different environments for robust wildlife monitoring applications using drones.

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