606 / 2019-03-29 10:47:35

Deep learning Model for Urban Land Use Classification using Call Detail Records

Urban Land Use,Deep learning,Convolutional Autoencoder Model,Call Detail Records

全体主题 > Artificial Neural Networks

Location-based information acquired from mobile phone data provides insights into the interactions between individuals and their urban environment such as urban land uses. Using CDRs data and the DCAE machine learning model, the study aims to recognize citywide land uses in a more accurate and timely manner, which can serve as important reference for urban planning practices. The proposed technique uses unsupervised learning based on deep convolutional autoencoder (DCAE) model and automatically recognizes land uses in urban areas by clustering geographical regions with similar features in curve patterns and volumes of mobile phone calls in a time series. A case study of Wuhan city is presented, and the land-use classification results are validated using CDRs data and land use information provided by the city planning departments. The study concludes that our method can more effectively recognize and classify land uses in the case city compared with previous methods.