Abstract
Urbanization is accelerating at a rapid rate, which has introduced many challenges, especially in the field of urban planning. Under the backdrop of global urbanization, some cities are particularly vulnerable to climate change and natural disasters that are influenced by unplanned urban expansion. Rational planning of urban functional areas needs to be strengthened to improve the scientific approach of urban planning and urbanization. In this study, the classification of urban functional areas based on dual-modal data (i.e., remote sensing image and user behavior data) was implemented using machine learning (ML) algorithms. After the set test, the classification accuracy of urban functional areas reached 82.45%. Through analysis, it could be concluded that the use of data of two modalities achieved a higher classification accuracy than that achieved by using data of a single modality. The data of the two modalities complement each other, and the use of ML algorithms to train such data can yield good results.
Highlights
T HE United Nations has proposed the 2030 Agenda for Sustainable Development to achieve sustainable development goals (SDGs) [1] in three dimensions: the society, economy, and environment
To reduce overfitting of the training model, the convolutional neural network (CNN) model was optimized through five-fold cross-validation
We propose a method based on the data from two modalities and machine learning (ML) algorithms to classify urban functional areas
Summary
T HE United Nations has proposed the 2030 Agenda for Sustainable Development to achieve sustainable development goals (SDGs) [1] in three dimensions: the society, economy, and environment. Urbanization plays a key role in each of these dimensions. Cities present a challenging and inspiring scope for achieving the SDGs, and urban planning is the key factor in realizing sustainable construction [2], [3]. With the development of the social economy, the influence of spatial pattern of functional zones in cities on the efficiency of life is increasing.
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