最近五年，卷积神经网络（CNN）得到了充分的发展，在图像分类领域，基于监督学习的算法在相关任务中取得了巨大的成功。但是与分类极为准确地粗粒度标签数据集相比，细粒度标签数据集的分类依旧是一个难点。地理图像被广泛应用于社会的各个方面，研究者往往需要对大规模的地理图像数据进行分类，但是由于地理图像的特征差异较小，因此自动化分类是相对困难的。对地理图像的细粒度特征进行标记，通过深度卷积网络对其进行训练和学习，极大地提高地理图像的分类精度。

algorithm based on supervised learning has achieved great success in related tasks. However, compared with coarse-grained label data sets, the classification of fine-grained label data sets is still a difficult point. Geographic images are widely used in all aspects of society. Researchers often need to classify large-scale geographic image data. However, due to the small feature difference of geographic images, automatic classification is relatively difficult. In this paper, the fine-grained features of geographic images are labeled, and trained and learned through the deep convolution network, which greatly improves the classification accuracy of geographic images.