Wang, S.;                     Zhu, Y.;                     Zheng, N.;                     Liu, W.;                     Zhang, H.;                     Zhao, X.;                     Liu, Y.    
        Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network. Remote Sens. 2024, 16, 1736.
    https://doi.org/10.3390/rs16101736
    AMA Style
    
                                Wang S,                                 Zhu Y,                                 Zheng N,                                 Liu W,                                 Zhang H,                                 Zhao X,                                 Liu Y.        
                Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network. Remote Sensing. 2024; 16(10):1736.
        https://doi.org/10.3390/rs16101736
    
    Chicago/Turabian Style
    
                                Wang, Shengli,                                 Yihu Zhu,                                 Nanshan Zheng,                                 Wei Liu,                                 Hua Zhang,                                 Xu Zhao,                                 and Yongkun Liu.        
                2024. "Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network" Remote Sensing 16, no. 10: 1736.
        https://doi.org/10.3390/rs16101736
    
    APA Style
    
                                Wang, S.,                                 Zhu, Y.,                                 Zheng, N.,                                 Liu, W.,                                 Zhang, H.,                                 Zhao, X.,                                 & Liu, Y.        
        
        (2024). Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network. Remote Sensing, 16(10), 1736.
        https://doi.org/10.3390/rs16101736