Using Multiple Sources of Data and “Voting Mechanisms” for Urban Land-Use Mapping

Kang Zheng, Huiyi Zhang, Haiying Wang (), Fen Qin, Zhe Wang and Jinyi Zhao
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Kang Zheng: The College of Geography and Environment Science, Henan University, Kaifeng 475004, China
Huiyi Zhang: The College of Geography and Environment Science, Henan University, Kaifeng 475004, China
Haiying Wang: The College of Geography and Environment Science, Henan University, Kaifeng 475004, China
Fen Qin: The College of Geography and Environment Science, Henan University, Kaifeng 475004, China
Zhe Wang: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
Jinyi Zhao: The College of Geography and Environment Science, Henan University, Kaifeng 475004, China

Land, 2022, vol. 11, issue 12, 1-18

Abstract: High-quality urban land-use maps are essential for grasping the dynamics and scale of urban land use, predicting future environmental trends and changes, and allocating national land resources. This paper proposes a multisample “voting mechanism” based on multisource data and random forests to achieve fine mapping of urban land use. First, Zhengzhou City was selected as the study area. Based on full integration of multisource features, random forests were used to perform the preliminary classification of multiple samples. Finally, the preliminary classification results were filtered according to the “voting mechanism” to achieve high-precision urban land-use classification mapping. The results showed that the overall classification accuracy of Level I features increased by 5.66% and 14.32% and that the overall classification accuracy of Level II features increased by 9.02% and 12.46%, respectively, compared with the classification results of other strategies. Therefore, this method can significantly reduce the influence of mixed distribution of land types and improve the accuracy of urban land-use classification at a fine scale.

Keywords: multisource data; random forests; urban land use; voting mechanisms (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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