AI in remote sensing and satellite image processing-a review

The integration of artificial intelligence (AI) in remote sensing and satellite image processing has significantly transformed the field, offering advanced tools for data analysis, feature extraction, and environmental monitoring. With the growing availability of high-resolution satellite imagery, AI applications such as machine learning and deep learning have been applied to automate the process of interpreting complex spatial data. This paper intends to explore the current state of AI in remote sensing, focused on some applications such as land cover classification, object detection, climate change monitoring, and disaster management. Additionally, the challenges and future directions for AI-driven remote sensing are discussed, emphasizing the need for better generalizability, data fusion, and improved computational efficiency.

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Publication Details

Journal
Environmental Earth Sciences
Published
2026-01-29
DOI
https://doi.org/10.1007/s12665-025-12798-w
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
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article

AI in remote sensing and satellite image processing-a review

Ondřej Krejcar, Hamidreza Namazi
Environmental Earth Sciences
Remote-Sensing Image Classification
article

AI in remote sensing and satellite image processing-a review

Ondřej Krejcar, Hamidreza Namazi
article en

Abstract

The integration of artificial intelligence (AI) in remote sensing and satellite image processing has significantly transformed the field, offering advanced tools for data analysis, feature extraction, and environmental monitoring. With the growing availability of high-resolution satellite imagery, AI applications such as machine learning and deep learning have been applied to automate the process of interpreting complex spatial data. This paper intends to explore the current state of AI in remote sensing, focused on some applications such as land cover classification, object detection, climate change monitoring, and disaster management. Additionally, the challenges and future directions for AI-driven remote sensing are discussed, emphasizing the need for better generalizability, data fusion, and improved computational efficiency.

Environmental Earth SciencesVol. 85(3)
Monash University Malaysia (MY), Škoda (Czechia) (CZ), Škoda Auto University (CZ), University of Hradec Králové (CZ)
Climate action
Openalex Percentile: Top 75%
Remote-Sensing Image Classification
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