Paper Info
Title
Inference Of Urban Function Zone Based On Deep Neural Network
Abstract
With the rapid development of urbanization, more and more attention has been paid to the structure of urban function zone. Thus, it is of great significance to investigate urban function zone. In this paper, we introduced the deep neural network (DNN) to infer the urban function zone with a supervised classification approach, taking the Shenzhen city in China as a case. First of all, the urban road networks of Shenzhen city were gathered and selected appropriately. Then, the fifth level road networks were utilized to segment the study region. Second, the communication data of different times and points of interest (POI) were collected. Then, the fifteen factors influencing urban function zone were derived. In addition, the urban function zone was divided into five types and the labeled examples with fifteen influencing factors were chosen. Third, the labeled examples were employed to train the DNN with different hidden layers compared with random forest (RF) and support vector machine (SVM). The models were trained with the approach of five-fold cross validation, and the average training accuracy with five times is taken as the accuracy of models. Finally, this paper compared the accuracy. It's been shown in the results that DNN was the optimum model and achieved the highest accuracy. Therefore, our proposed method is an efficient approach to infer the urban function zone.
Year
DOI
Venue
2020
10.1109/IGARSS39084.2020.9324580
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
Keywords
DocType
Citations 
Inference of urban function zone, DNN
Conference
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Ankai Hou100.34
Mingcang Zhu201.35
Pengshan Li300.34
Yong He401.01
Xiaobo Zhang500.68
Jibao Shi600.68
Kai Chen700.68
Tao Weng800.68
Zezhong Zheng92912.43
Zezhong Zheng102912.43