Detecting animals in African Savanna with UAVs and the crowds

Drones 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 research - based on the results produced by the SAVMAP project funded by CODEV and in the context of a master thesis carried out at LASIG - 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.

The paper was recently published in the journal Remote Sensing of Environment. It is freely available here until October 19, 2017, and the link will then be substituted with the arXiv version of the article.