  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.
