| Abstract |
Traditional green view rate (GVR) methods, which rely on two-dimensional planar images, have several limitations. They fail to capture the three-dimensional spatial characteristics of urban greenery, are frequently dependent on subjective parameters such as camera angles and lighting, and require labor-intensive manual analysis. These factors limit the accuracy and scalability of green space assessments. To overcome these challenges, this study introduces the Panoramic Green Perception Rate (PGPR). This novel metric utilizes spherical panoramic imagery and deep learning for the automated recognition of three-dimensional vegetation. A Dilated ResNet-105 network was used, achieving a mean Intersection over Union (mIoU) of 62.53% with only a 9.17% average deviation from manual annotation. PGPR was empirically applied in Ziyang Park, Wuhan, where it effectively quantified green visibility across urban activity spaces. This approach allows for the scalable and objective evaluation of urban greenery, which has practical applications in urban planning, landscape assessment, and ecological low-carbon construction. Urban planners, environmental engineers, and computer vision and smart city development researchers will find it especially useful. © (2025), (Science and Information Organization). All Rights Reserved. |
