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Stream/Area:
-
Subject:
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Paper ID:
10.55041/IJSREM.ISTCESD042
Volume:
10 |
9 |
Sep 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Geology,
Sanjeevanee Mahavidyalaya Chapoli
Department of Geology,
Sanjeevanee Mahavidyalaya Chapoli
The combination of artificial intelligence (AI) and remote sensing has brought revolutionary changes to the field of modern geoscience. This review explores the convergence of cutting-edge scientific advances in artificial intelligence and remote sensing with a focus on the earth sciences. The objective of this article is to identify how various machine learning techniques, neural networks, and multi-sensor systems are used to fill observational gaps in the fields. The discussion highlights the advantages and challenges associated with adopting these AI technologies. The review focuses on the changes in the industry and the market requirements for specialists with expertise in this field. A systematic method was used for this review following the Preferred Reporting Items for Systematic Review guidelines. Relevant articles and documents were searched and retrieved from reputable databases. The research study was conducted between 2018 and 2026. The results of the study indicate that advanced algorithms such as the transformers and physics-informed neural networks are more effective in analyzing the hyper spectral, SAR, and LIDAR signals compared to conventional models. On the other hand, the review also reveals some challenges and weaknesses associated with using AI in remote sensing. It also reports an increase in entrepreneurial activity in the last decade due to the rise of commercial earth imaging constellations. There is a need for people with specialized skills in geoscience and data science and collaboration between academic institutions, business entities, and government agencies to ensure quality results.
KEYWORDS
The integration of artificial intelligence and remote sensing represents a paradigm shift in modern geoscience. As highlighted in this thematic review, while novel architectures such as transformers and physics-informed neural networks circumvent many classical analytical limitations, challenges related to model interpretability persist. Addressing these issues requires not only algorithmic advances but also enhanced workforce readiness through interdisciplinary education linking geosciences, statistics, and software engineering. The convergence of AI-RS holds great promise, providing a much-needed analytical framework to address complex societal challenges such as climate change, sustainable resource management, and natural disaster prediction
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Publication Date
9/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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