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

Chuanrong Zhang () and Xinba Li
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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

Land, 2022, vol. 11, issue 10, 1-22

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.

Keywords: land use and land cover mapping; remote sensing; machine learning; deep learning; geospatial big data (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 references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:10:p:1692-:d:929822

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