Identification of Built-up Urban Areas in High-Resolution, Remotely- Sensed Images Based on Deep Learning

Authors

  • Chujun Li School of Engineering, Xi’an International University, Xi’an 710077, Shaanxi, China
  • Md Gapar School of Graduate Studies, Management and Science University, Shah Alam 40100, Malaysia
  • Md Johar Software Engineering and Digital Innovation Centre, Management and Science University, Shah Alam 40100, Malaysia

Abstract

Theaccuracy of traditional identification methods for built-up urban areas is often challenged when faced with complex urban environments comprising dense buildings and shadows, especially when identifying small targets and multi-scale building features. To address these problems, this paper constructs an built-up urban area identification model based on the Weighted Feature Fusion Enhanced UNet (WFFE-UNet). The data used in this paper isfrom the Massachusetts Buildings Dataset, whichprovides aerial images oftheBoston metropolitan areawith aresolution of1 meter. Histogram equalization and principal component analysis denoising techniques are used to improve image quality and help the model capture key information
in the image more accurately. Then, an identification model based on WFFE-UNet is constructed. Based on the UNet framework, this model uses an improved convolution module with multi-scale perception to enhance the ability to express multi-scale building structures in high-resolution images. The Convolutional Block Attention Module (CBAM) attention mechanism is integrated to improve the identification ability of small targets. By combining channel attention and spatial attention, the model’s sensitivity to the key features of small targets is significantly enhanced. Finally, the strategy of joint optimization of cross-entropy loss and Dice loss is adopted to improve the model’s convergence efficiency and identification precision. Experimental results show that the overall accuracy, precision, recall, and intersection over union (IoU) of the WFFE-UNet model are 97.34%, 96.76%,
95.42%, and 82.65%, respectively. In the cross-domain test, the overall identification accuracy of the model in different cities remains above 96%. For small targets B and P, the IoU of the model studied in this paper is 78.98% and 78.46%, respectively. The built-up urban area identification method based on WFFE-UNet and CBAM attention mechanism successfully solves the problems encountered by traditional methods when applied complex scenes by combining automatic feature learning, multi-scale information fusion, and an attention mechanism.

Keywords: deep learning, high-resolution remotely-sensed image, built-up urban area identification, Weighted Feature Fusion Enhanced UNet, Convolutional Block Attention Module

Cite As

C. Li, M. Gapar, M. Johar, "Identification of Built-up Urban Areas in High-Resolution, Remotely- Sensed Images Based on Deep Learning", Engineering Intelligent Systems, vol. 34 no. 3, pp. 361-374, 2026.

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Published

2026-05-01

Issue

Vol. 34 No. 3 (2026): International Journal of Engineering Intelligent Systems

Section

Article

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