Extracting urban spatial perception attributes and scene elements by integrating VGG-16 and CBAM

• DOI: 10.1007/s43762-025-00181-1
• Publication Date: 2025-04-29T04:05:41Z
• Authors: Jing Xu

Abstract As urbanization continues to accelerate, there is a growing need for the analysis of urban spatial perception attributes and scene elements. In response, research proposed multi-scale network-based model extracting elements an attention-enhanced segmentation analyzing structures. The feature extraction incorporated Siamese convolutional neural networks block attention achieve extraction. structure combined dynamic module with encoder-decoder architecture enhance accuracy element segmentation. During testing at resolution 768, its classification cross entropy were 95.4% 0.065, respectively. model's average ranking beauty, comfort, cleanliness was 92.5%, 91.8%, 93.2%. model, boundary intersection union ratio F1 score 81.2% 82.1%, respectively, complexity 0.6. results demonstrated that method excelled in tasks such as perceptual attribute parsing, effectively addressing complex diverse features.