摘要。随着中国城市化进程的快速推进，对城市土地利用模式的自动检测研究具有重要意义。深度学习是一种有效的图像特征提取方法。为充分利用深度学习方法在检测城市土地利用模式中的优势，本文采用基于迁移学习的遥感图像方法提取并分类特征。利用Google TensorFlow框架构建了一个强大的卷积神经网络（CNN）库。首先，将预训练于ImageNet——一个大型物体图像数据集——的模型用于充分开发其生成标准遥感地表覆盖数据集（UC Merced和WHU-SIRI）特征向量的能力。随后，基于生成的特征向量构建并训练了一个随机森林分类器，以实现交通分析区（TAZs）尺度上的实际城市土地利用模式分类。为避免遥感影像的多尺度效应，采用了大随机块（LRP）方法。所提出的方法在研究区域中能够高效获得可接受的分类精度（总体精度OA = 0.794，Kappa = 0.737）。此外，结果表明，该方法能有效克服在不规则地块层面进行城市土地利用分类时出现的多尺度效应。该方法可帮助规划者监测动态城市土地利用状况，并评估城市规划方案的影响。

Abstract. With the rapid progress of China’s urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model’s ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.