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There is a growing demand for accurate high-resolution land cover maps inmany 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 humansupervision and the best configuration for one image can hardly be extrapolatedto a different image. Recently, the deep learning Convolutional Neural Networks(CNNs) have shown outstanding results in object recognition in the field ofcomputer vision. However, they have not been fully explored yet in land covermapping for detecting species of high biodiversity conservation interest. Thispaper analyzes the potential of CNNs-based methods for plant species detectionusing free high-resolution Google Earth T M images and provides an objectivecomparison with the state-of-the-art OBIA-methods. We consider as case studythe detection of Ziziphus lotus shrubs, which are protected as a priorityhabitat under the European Union Habitats Directive. According to our results,compared to OBIA-based methods, the proposed CNN-based detection model, incombination with data-augmentation, transfer learning and pre-processing,achieves higher performance with less human intervention and the knowledge itacquires in the first image can be transferred to other images, which makes thedetection process very fast. The provided methodology can be systematicallyreproduced for other species detection.

Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery