Skip to main content

page search

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

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

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

Resource information

Date of publication
December 2021
Resource Language
ISBN / Resource ID
LP-midp003436

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.

Share on RLBI navigator
NO

Authors and Publishers

Author(s), editor(s), contributor(s)

Zheng, KangZhang, HuiyiWang, HaiyingQin, FenWang, ZheZhao, Jinyi

Corporate Author(s)
Geographical focus