Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 981-988, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-981-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
SENSING URBAN LAND-USE PATTERNS BY INTEGRATING GOOGLE TENSORFLOW AND SCENE-CLASSIFICATION MODELS
Y. Yao, H. Liang, X. Li, J. Zhang, and J. He Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, School of Geography and Planning, Guangzhou, Guangdong province, China
Keywords: Land use, Tensorflow, Scene Classification, Land Parcels, Deep Learnin 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.
Conference paper (PDF, 2060 KB)


Citation: Yao, Y., Liang, H., Li, X., Zhang, J., and He, J.: SENSING URBAN LAND-USE PATTERNS BY INTEGRATING GOOGLE TENSORFLOW AND SCENE-CLASSIFICATION MODELS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 981-988, https://doi.org/10.5194/isprs-archives-XLII-2-W7-981-2017, 2017.

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