Classifying and mapping vegetation are crucial tasks in environmental science and natural resource management. However, these tasks are difficult because conventional methods such as field surveys are highly labor-intensive. Identification of target objects from visual data using computer techniques is one of the most promising techniques to reduce the costs and labor for vegetation mapping. Although deep learning and convolutional neural networks (CNNs) have become a new solution for image recognition and classification recently, in general, detection of ambiguous objects such as vegetation is still difficult. In this study, we investigated the effectiveness of adopting the chopped picture method, a recently described protocol for CNNs, and evaluated the efficiency of CNN for plant community detection from Google Earth images. We selected bamboo forests as the target and obtained Google Earth images from three regions in Japan. By applying CNN, the best trained model correctly detected over 90% of the targets. Our results showed that the identification accuracy of CNN is higher than that of conventional machine learning methods. Our results demonstrated that CNN and the chopped picture method are potentially powerful tools for high-accuracy automated detection and mapping of vegetation.

中文翻译：

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使用卷积神经网络识别Google Earth图像中的植被类型：以日本竹林为例

对植被进行分类和制图是环境科学和自然资源管理中的关键任务。但是，这些任务是困难的，因为诸如田野调查的常规方法是高度劳动密集型的。使用计算机技术从视觉数据中识别目标物体是减少植被测绘成本和劳动力的最有前途的技术之一。尽管深度学习和卷积神经网络（CNN）最近已成为图像识别和分类的新解决方案，但总的来说，对诸如植被之类的模糊对象的检测仍然很困难。在这项研究中，我们调查了采用切碎图片方法（一种最近描述的CNN协议）的有效性，并评估了CNN从Google Earth图像中检测植物群落的效率。我们选择了竹林作为目标，并获得了日本三个地区的Google Earth图像。通过应用CNN，训练有素的模型可以正确检测出90％以上的目标。我们的结果表明，CNN的识别精度高于传统的机器学习方法。我们的结果表明，CNN和切碎的图片方法是用于高精度自动检测和绘制植被图的潜在强大工具。