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Automated rhinoceros detection in satellite imagery using deep learning.

Isla Duporge1, Xiaomin Lin2, Aadi Palnitkar3

  • 1Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. Isla.duporge@princeton.edu.

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Summary

Detecting white rhinoceroses using satellite imagery and AI is feasible, achieving 0.65 average precision. This technology aids conservation efforts for endangered rhinos in vast habitats.

Keywords:
Conservation TechnologyDeep LearningEarth ObservationObject DetectionPopulation EstimationSatellite Remote SensingWildlife Monitoring

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Area of Science:

  • Conservation Technology
  • Remote Sensing
  • Wildlife Monitoring

Background:

  • Rhinoceros populations face critical threats from poaching and habitat loss.
  • Monitoring rhinoceroses in extensive, remote areas presents significant logistical challenges.

Purpose of the Study:

  • To evaluate the effectiveness of very high-resolution satellite imagery and AI for detecting white rhinoceroses.
  • To assess the impact of synthetic data augmentation on detection model performance.
  • To determine if rhinoceroses can be differentiated from elephants in satellite imagery.

Main Methods:

  • Utilized a YOLO-based object detection model (YOLOv12x) with satellite imagery (33-36 cm resolution).
  • Tested synthetic imagery to enhance model performance and distinguish rhinoceroses from elephants.
  • Evaluated the visual distinguishability of synthetic versus real rhinoceros images by human annotators.

Main Results:

  • Achieved an average precision (AP) of 0.65 for rhinoceros detection.
  • Synthetic data augmentation provided a marginal improvement in model performance.
  • Demonstrated the potential for differentiating rhinoceroses from elephants in satellite data.

Conclusions:

  • Satellite imagery combined with AI offers a viable method for monitoring rhinoceros populations.
  • The developed open-access dataset supports advancements in wildlife detection technologies.
  • This approach can bolster rhino conservation strategies, including anti-poaching and population assessments.