Modeling urban redevelopment: A novel approach using time-series remote sensing data and machine learning

Abstract

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decision-makers to foster sustainable urban development. Traditional mapping methods heavily depend on field surveys and subjective questionnaires, yielding less objective, reliable, and timely data. Recent advancements in Geographic Information Systems (GIS) and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations. Nonetheless, challenges persist, particularly concerning accuracy and significant temporal delays. This study introduces a novel approach to modeling urban redevelopment, leveraging machine learning algorithms and remote-sensing data. This methodology can facilitate the accurate and timely identification of urban redevelopment activities. The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment. The model is thoroughly evaluated, and the results indicate that it can accurately capture the time-series patterns of urban redevelopment. This research’s findings are useful for evaluating urban demographic and economic changes, informing policymaking and urban planning, and contributing to sustainable urban development. The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Keywords

Urban redevelopment / Urban sustainability / Remote sensing / Time-series analysis / Machine learning

Declaration of conflicting interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to thank the editors and two anonymous referees for their valuable comments and suggestions.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2024.02.001.

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