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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.

We present a theoretical study on the behavior under pressure of the two hypothetical C$_{46}$ and Li$_8$C$_{46}$ type-I carbon clathrates in order to bring new informations concerning their synthesis. Using \textit{ab initio} calculations, we have explored the energetic and structural properties under pressure of these two carbon based cage-like materials. These low-density meta-stable phases show large negative pressure transitions compared to diamond which represent a serious obstacle for their synthesis. However, we evidence that a minimum energy barrier can be reached close to 40 GPa, suggesting that the synthesis of the Li-clathrate under extreme conditions of pressure and temperature may be possible. Electronic band structure with related density of states behavior under pressure as well as the dependence of the active Raman modes with pressure are also examined.