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

被引：16

作者：

Zhang, Chuanrong ^{[1
,2
]}

Li, Xinba ^{[3
]}

机构：

[1] Univ Connecticut, Dept Geog, Storrs, CT 06269 USA

[2] Univ Connecticut, Ctr Environm Sci & Engn, Storrs, CT 06269 USA

[3] Vanderbilt Univ, Dept Econ, Nashville, TN 37235 USA

来源：

LAND | 2022年 / 11卷 / 10期

基金：

美国国家科学基金会;

关键词：

land use and land cover mapping; remote sensing; machine learning; deep learning; geospatial big data; GOOGLE EARTH ENGINE; HYPERSPECTRAL IMAGE CLASSIFICATION; NEURAL-NETWORKS; STREET VIEW; AIRBORNE LIDAR; URBAN; RESOLUTION; OPENSTREETMAP; AREA; MAP;

D O I：

10.3390/land11101692

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：22

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