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Detecting animals in African Savanna with UAVs and the crowds

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Rey Nicolas, Volpi Michele, Joost Stéphane, Tuia Devis,
Project Multimodal machine learning for remote sensing information fusion
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Original article (peer-reviewed)

Journal Remote Sensing of Environment
Volume (Issue) 200
Page(s) 341 - 351
Title of proceedings Remote Sensing of Environment
DOI 10.1016/j.rse.2017.08.026

Open Access

URL https://arxiv.org/abs/1709.01722
Type of Open Access Repository (Green Open Access)

Abstract

© 2017 Elsevier Inc. Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub-decimeter resolution color images. The system achieves a high recall rate and a human operator can then eliminate false detections with limited effort. Our system provides good perspectives for the development of data-driven management practices in wildlife conservation. It shows that the detection of large mammals in semi-arid Savanna can be approached by processing data provided by standard RGB cameras mounted on affordable fixed wings UAVs.
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