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Land Use and Land Cover Mapping in the Era of Big Data

Author

Listed:
  • Chuanrong Zhang

    (Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Xinba Li

    (Department of Economics, Vanderbilt University, Nashville, TN 37235, USA)

Abstract

We are currently living in the era of big data. The volume of collected or archived geospatial data for land use and land cover (LULC) mapping including remotely sensed satellite imagery and auxiliary geospatial datasets is increasing. Innovative machine learning, deep learning algorithms, and cutting-edge cloud computing have also recently been developed. While new opportunities are provided by these geospatial big data and advanced computer technologies for LULC mapping, challenges also emerge for LULC mapping from using these geospatial big data. This article summarizes the review studies and research progress in remote sensing, machine learning, deep learning, and geospatial big data for LULC mapping since 2015. We identified the opportunities, challenges, and future directions of using geospatial big data for LULC mapping. More research needs to be performed for improved LULC mapping at large scales.

Suggested Citation

  • Chuanrong Zhang & Xinba Li, 2022. "Land Use and Land Cover Mapping in the Era of Big Data," Land, MDPI, vol. 11(10), pages 1-22, September.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:10:p:1692-:d:929822
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    References listed on IDEAS

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    2. Qingfeng Guan & Sijing Cheng & Yongting Pan & Yao Yao & Wen Zeng, 2021. "Sensing Mixed Urban Land-Use Patterns Using Municipal Water Consumption Time Series," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 111(1), pages 68-86, January.
    3. Xia Li & Guangzhao Chen & Xiaoping Liu & Xun Liang & Shaojian Wang & Yimin Chen & Fengsong Pei & Xiaocong Xu, 2017. "A New Global Land-Use and Land-Cover Change Product at a 1-km Resolution for 2010 to 2100 Based on Human–Environment Interactions," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(5), pages 1040-1059, September.
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    Cited by:

    1. Andrew Clark & Stuart Phinn & Peter Scarth, 2023. "Pre-Processing Training Data Improves Accuracy and Generalisability of Convolutional Neural Network Based Landscape Semantic Segmentation," Land, MDPI, vol. 12(7), pages 1-25, June.

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