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Sensors and Materials, Volume 38, Number 7(1) (2026)
Copyright(C) MYU K.K.

pp. 3713-3720
S&M4530 Research paper
https://doi.org/10.18494/SAM5284
Published: July 10, 2026

Intelligent Extraction of Urban Vegetation Information Based on Segment Anything Model and Residual Neural Network Model [PDF]

Yuan Zhuang, Yi Zhang, Shuang Wu, Haizhuo Sun, Yanfeng Xie, Siyang Yin, and Chunyang Cui

(Received August 6, 2024; Accepted June 3, 2026)

Keywords: high-resolution remote sensing image, deep learning, urban vegetation information, SAM and ResNet model, extraction

To effectively support urban ecological environment assessment and gross ecosystem product (GEP) calculation, we used Beijing-2 and Beijing-3 satellite remote sensing images with a resolution of 0.8 m as data sources and a self-made urban vegetation sample dataset, and proposed a model combining the Segment Anything Model (SAM) and residual neural network (ResNet) for vegetation extraction. This method effectively combined the segmentation ability of SAM with the classification and extraction ability of the ResNet model. The model training results showed that the MIoU, MPA, and ACC of this method reached 77.27, 84.74, and 85.08%, respectively, which were slightly better than those of the ResNet model, and it can accurately segment and extract the microvegetation pattern in a complex urban environment.

Corresponding author: Yi Zhang

This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Yuan Zhuang, Yi Zhang, Shuang Wu, Haizhuo Sun, Yanfeng Xie, Siyang Yin, and Chunyang Cui, Intelligent Extraction of Urban Vegetation Information Based on Segment Anything Model and Residual Neural Network Model , Sens. Mater., Vol. 38, No. 7, 2026, p. 3713-3720.