Incorporating Building Morphology Data to Improve Urban Land Use Mapping: A Case Study of Shenzhen

Abstract

Accurate urban land use classification is vital for urban planning, resource allocation, and sustainable management. Traditional remote sensing methods struggle with fine-grained classification and spatial structure identification, while socio-economic data, like points of interest and road networks, face issues of uneven distribution and outdated updates. To explore the role of building morphology characteristics in enhancing urban land use classification and their potential as a substitute for socio-economic information, this study proposes a method integrating architectural features with multi-source remote sensing data, evaluated through an empirical analysis using a random forest model in Shenzhen. Three models were developed as follows: Model 1, utilizing only remote sensing data; Model 2, combining remote sensing with socio-economic data; and Model 3, integrating building morphology with remote sensing data to evaluate its potential for enhancing classification accuracy and substituting socio-economic data. Experimental results demonstrate that Model 3 achieves an overall accuracy of 80.09% and a Kappa coefficient of 0.77. Compared to this, Model 1 achieves an accuracy of 74.56% and a Kappa coefficient of 0.70, while Model 2 reaches 79.56% accuracy and a Kappa coefficient of 0.76. Model 3 also shows greater stability in complex, smaller parcels. This method offers superior generalization and substitution potential in data-scarce, heterogeneous contexts, providing a scalable approach for fine-grained urban monitoring and dynamic management.