IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i12p2209-d994067.html
   My bibliography  Save this article

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

Author

Listed:
  • Kang Zheng

    (The College of Geography and Environment Science, Henan University, Kaifeng 475004, China
    Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China
    These authors contributed equally to this work.)

  • Huiyi Zhang

    (The College of Geography and Environment Science, Henan University, Kaifeng 475004, China
    Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China
    These authors contributed equally to this work.)

  • Haiying Wang

    (The College of Geography and Environment Science, Henan University, Kaifeng 475004, China
    Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China
    Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng 475004, China
    Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China)

  • Fen Qin

    (The College of Geography and Environment Science, Henan University, Kaifeng 475004, China
    Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China
    Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng 475004, China
    Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, 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
    Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China)

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.

Suggested Citation

  • Kang Zheng & Huiyi Zhang & Haiying Wang & Fen Qin & Zhe Wang & Jinyi Zhao, 2022. "Using Multiple Sources of Data and “Voting Mechanisms” for Urban Land-Use Mapping," Land, MDPI, vol. 11(12), pages 1-18, December.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2209-:d:994067
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/12/2209/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/12/2209/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2209-:d:994067. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.