Artificial Intelligence in Remote Sensing: Advancements, Challenges, and Future Directions for Sustainable Applications

This chapter explores how artificial intelligence (AI) can be incorporated into remote sensing, emphasizing how it can revolutionize a number of fields, such as agriculture, urban planning, disaster relief, and environmental monitoring. It gives a summary of how artificial intelligence (AI) methods, especially machine learning and deep learning, improve the handling, interpretation, and use of data from remote sensing. The chapter also explores the main obstacles preventing AI from being widely used in remote sensing, including issues with data accessibility, model interpretability, training complexity, and ethical considerations. It also offers a number of real-world examples that show how AI can be used to provide useful insights and facilitate data-driven decision-making. In addition to outlining future directions for research, development, and responsible implementation, this chapter provides a balanced perspective on the changing role of AI in remote sensing by addressing both the opportunities and limitations.

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Publication Details

Journal
Advances in Research
Published
2025-06-06
DOI
https://doi.org/10.9734/air/2025/v26i31363
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial Intelligence in Remote Sensing: Advancements, Challenges, and Future Directions for Sustainable Applications

M Dhanapriya
Advances in Research
Remote-Sensing Image Classification
article

Artificial Intelligence in Remote Sensing: Advancements, Challenges, and Future Directions for Sustainable Applications

M Dhanapriya
article en

Abstract

This chapter explores how artificial intelligence (AI) can be incorporated into remote sensing, emphasizing how it can revolutionize a number of fields, such as agriculture, urban planning, disaster relief, and environmental monitoring. It gives a summary of how artificial intelligence (AI) methods, especially machine learning and deep learning, improve the handling, interpretation, and use of data from remote sensing. The chapter also explores the main obstacles preventing AI from being widely used in remote sensing, including issues with data accessibility, model interpretability, training complexity, and ethical considerations. It also offers a number of real-world examples that show how AI can be used to provide useful insights and facilitate data-driven decision-making. In addition to outlining future directions for research, development, and responsible implementation, this chapter provides a balanced perspective on the changing role of AI in remote sensing by addressing both the opportunities and limitations.

Advances in ResearchVol. 26(3)
Birla Institute of Technology, Mesra (IN)
Openalex Percentile: Top 82%
Remote-Sensing Image Classification
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