Detecting animals in African Savanna with UAVs and the crowds

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

Semantic labeling of aerial images by learning class-specific object proposals

Land-cover and land-use semantic labeling in centimeter resolution imagery (ultra-high resolution) is mostly performed by supervised classification of informative descriptors extracted from spatially coherent but small objects (e.g. superpixels or patches). In this paper, we propose an extension of this reasoning by proposing a class-specific, multi-scale and bottom-up object proposal strategy to perform semantic labeling. Specifically, we rely on a fully trainable boundary (edge) detector, allowing us to extract class-specific object-proposals. Such proposals enable training rich appearance and object models as well as enhanced spatial reasoning. We evaluate the proposed strategy on the Vaihingen dataset with promising results