Deep Learning for Remote Sensing Applications

Moung Jin Lee, Won Jin Lee, Seung Kuk Lee, Hyung Sup Jung

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations


Recently, deep learning has become more important in remote sensing data processing. Huge amounts of data for artificial intelligence (AI) has been designed and built to develop new technologies for remote sensing, and AI models have been learned by the AI training dataset. Artificial intelligence models have developed rapidly, and model accuracy is increasing accordingly. However, there are variations in the model accuracy depending on the person who trains the AI model. Eventually, experts who can train AI models well are required more and more. Moreover, the deep learning technique enables us to automate methods for remote sensing applications. Methods having the performance of less than about 60% in the past are now over 90% and entering about 100%. In this special issue, thirteen papers on how deep learning techniques are used for remote sensing applications will be introduced.

Original languageEnglish
Pages (from-to)1581-1587
Number of pages7
JournalKorean Journal of Remote Sensing
Issue number6
StatePublished - Dec 2022


  • Artificial intelligence
  • Deep learning
  • Remote sensing


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