  There is a growing demand for accurate high-resolution land cover maps in
many fields, e.g., in land-use planning and biodiversity conservation.
Developing such maps has been performed using Object-Based Image Analysis
(OBIA) methods, which usually reach good accuracies, but require a high human
supervision and the best configuration for one image can hardly be extrapolated
to a different image. Recently, the deep learning Convolutional Neural Networks
(CNNs) have shown outstanding results in object recognition in the field of
computer vision. However, they have not been fully explored yet in land cover
mapping for detecting species of high biodiversity conservation interest. This
paper analyzes the potential of CNNs-based methods for plant species detection
using free high-resolution Google Earth T M images and provides an objective
comparison with the state-of-the-art OBIA-methods. We consider as case study
the detection of Ziziphus lotus shrubs, which are protected as a priority
habitat under the European Union Habitats Directive. According to our results,
compared to OBIA-based methods, the proposed CNN-based detection model, in
combination with data-augmentation, transfer learning and pre-processing,
achieves higher performance with less human intervention and the knowledge it
acquires in the first image can be transferred to other images, which makes the
detection process very fast. The provided methodology can be systematically
reproduced for other species detection.
