Deep convolutional autoencoder for urban land use classification using mobile device data

Abstract

Mobile phone data can provide insightful location-based information on the interactions between individuals and the urban environment, e.g. urban land-use types. Using mobile phone data, this study aims to recognize citywide land use classes in a highly accurate and timely manner, because land use classes are important for urban planning and urban management. How to extract key and discriminative features from mobile-phone-call time series is an essential problem for urban land-use classification. Considering the high dimensional characteristic of mobile-phone-call time series data, an improved land-use classification framework with deep convolutional autoencoder (DCAE) is proposed to automatically recognize land-use types in urban areas, which are geographical regions clustered based on the similarity of features in waveform and magnitude of mobile-phone-call time series. The method is validated using mobile phone data collected in Wuhan city, China. The experimental result shows that the key and discriminative features in waveform and magnitude can be extracted without human prior knowledge and these features extracted by DCAE can improve the performance to distinguish functionally similar land-use types, such as business and industrial/commercial types.

Keywords:

• Urban land-use classification
• deep convolutional autoencoder model
• mobile phone data
• call detail records

Acknowledgments

We thank the editors and the anonymous reviewers for their valuable comments and suggestion.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available in [https://zenodo.org/] with the identifier(s) at the private link (https://doi.org/10.5281/zenodo.6513201).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was funded by the National Key Research and Development Program “Research and Development of Emergency Response and Collaborative Command System with Holographic Perception of Traffic Network Disaster” (Grant No. 2020YFC1512002) and the National Natural Science Foundation of China Major Program (Grant Nos. 42192580 and 42192583) and the project named “National Natural Science Foundation of China” (Grant No. 51978535).

Notes on contributors

Zhihao Sun

Zhihao Sun is a PH.D. candidate in Urban and Rural Planning at Wuhan University. His main research interests include urban sustainability, artificial intelligence, and spatial data mining. His contribution to this paper: processing data, designing algorithm, coding, analyzing results, writing manuscript.

Zhenghong Peng

Zhenghong Peng is a professor of Graphics and Digital Technology at School of Urban Design, Wuhan University. His research interests include artificial intelligence, data mining, urban development, and urban sustainability.

Yang Yu

Yang Yu is a Lecturer of Department of Urban Planning, School of Urban Design, Wuhan University. His research interests include Big Data and ABM applications in transportation, city and regional planning. His contribution to this paper: writing—original draft preparation and visualization.

Hongzan Jiao

Hongzan Jiao received the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 2013. He is an associate professor in the School of Urban Design at Wuhan University. His research interests include urban remote sensing, urban big data mining, urban computing, pattern recognition, and artificial intelligence, including genetic algorithm and deep learning network. His contribution to this paper: conceptualization, methodology, writing—review and editing, supervision, project administration and funding acquisition.