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An efficient built-up land expansion model using a modified U-Net

International Journal of Digital Earth 2022年第1期15卷 148-163页

作者： Hanieh Shojaei saeed nadi Hossein Shafizadeh-Moghadam Amin Tayyebi John Van Genderen Department of Geomatics Engineering University of IsfahanIsfahanIran Department of Civil and Environmental Engineering Carleton UniversityOttawaCanada Department of Water Resources Engineering and Management Tarbiat Modares UniversityTehranIran Data Science Manager Exelon UtilityChicagoILUSA Department of Earth Observation Sciences (ITC) University of TwenteEnschedeThe Netherlands

This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land *** proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urbanization as data *** variables including altitude,slope,and distance from barren,crop,greenery,roads,and urban areas for 1998,2008,and 2018 were considered as covariates for the simulation of built-up land expansion in Tehran and Karaj regions in *** proposed method was compared with the random forest(RF)algorithm as the baseline *** using the area under the total operating characteristic indicated the superiority of our modified U-Net(0.87)over the RF(0.82)***,evaluation using the percent correct metric indicated that our proposed model is capable of learning neighborhood effects effectively leading to simulate built-up land expansion accurately,independent from applying a cellular automata(CA)***,the modified U-Net independent from the CA which can consider the neighborhood effects is recommended for the simulation of built-up land expansion precisely.

关键词： Land use and land cover change built-up land expansion convolutional neural networks pixel-wise semantic segmentation U-Net total operating characteristic(TOC)

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