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

Autor: Hanieh Shojaei, Saeed Nadi, Hossein Shafizadeh-Moghadam, Amin Tayyebi, John Van Genderen
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: International Journal of Digital Earth, Vol 15, Iss 1, Pp 148-163 (2022)
Druh dokumentu: article
ISSN: 1753-8947
1753-8955
17538947
DOI: 10.1080/17538947.2021.2017035
Popis: This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land expansion. The proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urbanization as data cubes. Independent 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 Iran. The proposed method was compared with the random forest (RF) algorithm as the baseline model. Evaluation using the area under the total operating characteristic indicated the superiority of our modified U-Net (0.87) over the RF (0.82) algorithm. Furthermore, 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) model. Therefore, 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.
Databáze: Directory of Open Access Journals
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