Automatic vegetation identification in Google Earth images using a convolutional neural network: A case study for Japanese bamboo forests

ABSTRACT Classifying and mapping vegetation are very important tasks in environmental science and natural resource management. However, these tasks are not easy because conventional methods such as field surveys are highly labor intensive. Automatic identification of target objects from visual data is one of the most promising ways to reduce the costs for vegetation mapping. Although deep learning has become a new solution for image recognition and classification recently, in general, detection of ambiguous objects such as vegetation still is considered difficult. In this paper, we investigated the potential for adapting the chopped picture method, a recently described protocol for deep learning, to detect plant communities in Google Earth images. We selected bamboo forests as the target. We obtained Google Earth images from three regions in Japan. By applying the deep convolutional neural network, the model successfully learned the features of bamboo forests in Google Earth images, and the best trained model correctly detected 97% of the targets. Our results show that identification accuracy strongly depends on the image resolution and the quality of training data. Our results also highlight that deep learning and the chopped picture method can potentially become a powerful tool for high accuracy automated detection and mapping of vegetation.