Land Use and Land Cover Classification Meets Deep Learning: A Review

Shengyu Zhao, Kaiwen Tu, Shutong Ye, Hao Tang, Yaocong Hu, Chao Xie

Sensors 2023 article cited by 123

Source Open-access PDF More in Climate & Environment

importance 6/10 ai assisted Remote-Sensing Image Classification remote sensing stage: research

Problem

Classifying land use and land cover from remote sensing images is difficult because the data is high-dimensional and labeled samples are limited.

LULC information underpins environmental protection, urban planning, and land resource management.

This is a systematic review of deep-learning-based LULC classification methods; it surveys the field rather than presenting a new solving system.

not stated in source

The abstract states deep learning has achieved remarkable results and brought new possibilities to LULC classification research.

Venue	Sensors
Published	2023
Work type	article
Authors	Shengyu Zhao, Kaiwen Tu, Shutong Ye, Hao Tang, Yaocong Hu, Chao Xie
Citations	123 (OpenAlex, at import time)
References	104
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/s23218966
Open access	gold
Keywords	land use, land cover, deep learning, remote sensing, image classification, review

6 / 10

significant — thousands to millions affected

LULC classification underpins environmental protection, urban planning, and land resource management per abstract; no scale quantified.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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