Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network

Wenfei Luan, Ge Li (), Bo Zhong, Jianwei Geng, Xin Li, Hui Li and Shi He
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Wenfei Luan: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Ge Li: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Bo Zhong: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Jianwei Geng: Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Xin Li: National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Hui Li: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Shi He: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China

Land, 2023, vol. 12, issue 8, 1-20

Abstract: Reliable information of land cover dynamics in dryland cities is crucial for understanding the anthropogenic impacts on fragile environments. However, reduced classification accuracy of dryland cities often occurs in global land cover data. Although many advanced classification techniques (i.e., convolutional neural networks (CNN)) have been intensively applied to classify urban land cover because of their excellent performance, specific classification models focusing on typical dryland cities are still scarce. This is mainly attributed to the similar features between urban and non-urban areas, as well as the insufficient training samples in this specific region. To fill this gap, this study trained a CNN model to improve the urban land classification accuracy for seven dryland cities based on rigorous training sample selection. The assessment showed that our proposed model performed with higher overall accuracy (92.63%) than several emerging land cover products, including Esri 2020 Land Cover (75.55%), GlobeLand30 (73.24%), GLC_FCS30-2020 (69.68%), ESA WorldCover2020 (64.38%), and FROM-GLC 2017v1 (61.13%). In addition, the classification accuracy of the dominant land types in the CNN-classified data exceeded the selected products. This encouraging finding demonstrates that our proposed architecture is a promising solution for improving dryland urban land classification accuracy and compensating the deficiency of large-scale land cover mapping.

Keywords: dryland region; urban land classification; convolution neural network; training sample (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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