Integration of Artificial Intelligence and Remote Sensing: Review of the Progress, Problems and Prospects
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375-390
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10.58578/amjsai.v3i3.11822
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Authors:Thomas U. Omali1*
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Abstract
The integration of artificial intelligence (AI) and remote sensing (RS) has transformed Earth observation (EO) by enabling the automated, efficient, and precise analysis of large and complex datasets. Rapid advances in machine learning (ML) and deep learning (DL) have further enhanced the processing and interpretation of RS data. This review examines the progress, persistent challenges, and future prospects of integrating AI with RS. Relevant literature was identified through searches of Scopus, Web of Science, IEEE Xplore, and Google Scholar using combinations of terms related to AI, ML, DL, and RS. The reviewed literature indicates that AI substantially improves the efficiency and precision of RS data processing and interpretation while expanding opportunities for automation and data-driven decision-making. Nevertheless, the broader adoption of AI-driven RS remains constrained by data quality and heterogeneity, computational demands, limited model generalisability and explainability, and ethical concerns. Future research should prioritise model efficiency, interpretability, and adaptability, alongside multimodal learning, unsupervised and semi-supervised learning, and real-time AI deployment for global-scale applications. This review consolidates current advances and limitations in AI-enabled RS and provides a research agenda for developing more efficient, interpretable, and flexible models for Earth observation applications.
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References
Abdelmajeed, A. Y. A., & Juszczak, R. (2024). Challenges and limitations of remote sensing applications in northern peatlands: Present and future prospects. Remote Sensing, 16, 591. https://doi.org/10.3390/rs16030591
Akhtar, Z. B., & Rawol, A. T. (2025). Harnessing artificial intelligence (AI) towards the landscape of big earth data: Methods, challenges, opportunities, future directions. Journal of Geography and Cartography, 8(1), 10224. https://doi.org/10.24294/jgc10224
Akhyar, A., Asyraf Zulkifley, M., Lee, J., Song, T., Han, J., Cho, C., Hyun, S., Son, Y., & Hong, B.-W. (2024). Deep artificial intelligence applications for natural disaster management systems: A methodological review. Ecological Indicators, 163, 112067. https://doi.org/10.1016/j.ecolind.2024.112067
Almohsen, A. S. (2024). Challenges facing the use of remote sensing technologies in the construction industry: A review. Buildings, 14(9), 2861. https://doi.org/10.3390/buildings14092861
Bashmal, L., Bazi, Y., Melgani, F., Al Rahhal, M. M., & Al Zuair, M. A. (2023). Language integration in remote sensing: Tasks, datasets, and future directions. IEEE Geoscience and Remote Sensing Magazine, 11, 63–93.
Berg, P., Uzun, B., Pham, M.-T., & Courty, N. (2024). Multimodal supervised contrastive learning in remote sensing downstream tasks. IEEE Geoscience and Remote Sensing Letters, 21, 6007405. https://doi.org/10.1109/LGRS.2024.3385995
Bernardi, F. A., Alves, D., Crepaldi, N., Yamada, D. B., Lima, V. C., & Rijo, R. (2023). Data quality in health research: Integrative literature review. Journal of Medical Internet Research, 25, e41446. https://doi.org/10.2196/41446
Bortnyk, K., Yaroshchuk, B., Bahnuk, N., & Pekh, P. (2023). Overcoming challenges in artificial intelligence training: Data limitations, computational costs and model robustness. Computer-Integrated Technologies: Education, Science, Production, 53, 37–43.
Cao, Y. (2025). Using AI and big data analytics to support entrepreneurial decisions in the digital economy. Scientific Reports, 15, 36933. https://doi.org/10.1038/s41598-025-20871-4
Chaurasia, A. N., Parmar, R. M., Dave, M. G., & Krishnayya, N. S. R. (2024). Integrating field- and remote sensing data to perceive species heterogeneity across a climate gradient. Scientific Reports, 14, 42.
Contreras, J., Winterfeld, A., Popp, J., & Bocklitz, T. (2024). Spectral zones-based SHAP/LIME: Enhancing interpretability in spectral deep learning models through grouped feature analysis. Analytical Chemistry, 96, 15588–15597.
Dantas, P. V., Sabino da Silva, W., Cordeiro, L. C., & Carvalho, C. B. (2024). A comprehensive review of model compression techniques in machine learning. Applied Intelligence, 54, 11804–11844.
Dobson, J. E. (2023). On reading and interpreting black box deep neural networks. International Journal of Digital Humanities, 5, 431–449.
Elhamied, E. M. A., Youssef, S. M., & Shenawy, M. E. (2024). Improved model integrating FPN with refined IoU for efficient object detection algorithm in remote sensing satellite images. In Proceedings of the International Conference on Machine Intelligence and Smart Innovation (pp. 244–250).
Elwahab, F., Ziri, R., Boukita, H., & Sedki, M. Applications of artificial intelligence and remote sensing in environmental and agricultural engineering: A comprehensive review. ISPEC Journal of Science Institute, 4(2), 202–209. https://doi.org/10.5281/zenodo.18061813
Eran, D., Itzhak, A., & Tzvi, D. (2025). Aerial imagery redefined: Next-generation approach to object classification. Information, 16(2), 134. https://doi.org/10.3390/info16020134
Fabbri, A. G., Fung, K. B., & Yatabe, S. M. (1988). The usage of artificial intelligence in remote sensing: A review of applications and current research. In C. F. Chung & A. G. Fabbri (Eds.), Quantitative analysis of mineral and energy resources (Vol. 223, pp. 489–512). Springer.
Garg, P., Chakravarthy, A. S., Mandal, M., Narang, P., Chamola, V., & Guizani, M. (2021). ISDNet: AI-enabled instance segmentation of aerial scenes for smart cities. ACM Transactions on Internet Technology, 21(3), 1–18.
Gholizade, M., Soltanizadeh, H., Rahmanimanesh, M., & Sana, S. S. (2025). A review of recent advances and strategies in transfer learning. International Journal of System Assurance Engineering and Management, 16(3), 1123–1162. https://doi.org/10.1007/s13198-024-02684-2
Gu, Z., & Zeng, M. (2024). The use of artificial intelligence and satellite remote sensing in land cover change detection: Review and perspectives. Sustainability, 16, 274.
Gunther, A., Najjar, H., & Dengel, A. (2024). Explainable multimodal learning in remote sensing: Challenges and future directions. IEEE Geoscience and Remote Sensing Letters, 21, 8002405. https://doi.org/10.1109/LGRS.2024.3404596
Han, K., Kim, Y., Han, D., Lee, H., & Hong, S. (2023). TL-ADA: Transferable loss-based active domain adaptation. Neural Networks, 161, 670–681.
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024). Interpreting black-box models: A review on explainable artificial intelligence. Cognitive Computation, 16, 45–74.
Jamil Ghilzai, R., Siddique, M., Rubab, S., & Rasool, K. (2025). Advances in AI-based land use and land cover classification: A review of deep learning and remote sensing integration. International Journal of Innovations in Science & Technology, 7, 2066–2090. https://doi.org/10.33411/ijist/20257320662090
Janga, B., Asamani, G. P., Sun, Z., & Cristea, N. (2023). A review of practical AI for remote sensing in earth sciences. Remote Sensing, 15, 4112.
Janga, J. K., Reddy, K. R., & Raviteja, K. V. N. S. (2023). Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review. Chemosphere, 345, 140476. https://doi.org/10.1016/j.chemosphere.2023.140476
Kazanskiy, N., Khabibullin, R., Nikonorov, A., & Khonina, S. (2025). A comprehensive review of remote sensing and artificial intelligence integration: Advances, applications, and challenges. Sensors, 25(19), 5965. https://doi.org/10.3390/s25195965
Kothari, N. S., & Meher, S. K. (2020). Semi-supervised classification of remote sensing images using efficient neighbourhood learning method. Engineering Applications of Artificial Intelligence, 90, 103520.
Li, J., Hong, D., Gao, L., Yao, J., Zheng, K., Zhang, B., & Chanussot, J. (2022). Deep learning in multimodal remote sensing data fusion: A comprehensive review. International Journal of Applied Earth Observation and Geoinformation, 112, 102926.
Li, Y., Li, Z., Wang, Z., Wang, K., & Yu, Q. (2024). Semi-supervised remote sensing image scene classification with prototype-based consistency. Chinese Journal of Aeronautics, 37, 459–470.
Li, Z., Li, H., & Meng, L. (2023). Model compression for deep neural networks: A survey. Computers, 12, 60.
Liu, D., Zhu, Y., Liu, Z., Liu, Y., Han, C., Tian, J., Li, R., & Yi, W. (2025). A survey of model compression techniques: Past, present, and future. Frontiers in Robotics and AI, 12, 1518965. https://doi.org/10.3389/frobt.2025.1518965
Liu, P., Wang, L., & Li, J. (2023). Unlocking the potential of explainable artificial intelligence in remote sensing big data. Remote Sensing, 15, 5448.
Miller, T., Durlik, I., Kostecka, E., Kozlovska, P., Łobodzińska, A., Sokołowska, S., & Nowy, A. (2025). Integrating artificial intelligence agents with the Internet of Things for enhanced environmental monitoring: Applications in water quality and climate data. Electronics, 14(4), 696. https://doi.org/10.3390/electronics14040696
Mohseni, S., Zarei, N., & Ragan, E. D. (2021). A multidisciplinary survey and framework for design and evaluation of explainable AI systems. ACM Transactions on Interactive Intelligent Systems, 11(3–4). https://doi.org/10.1145/3387166
Navaux, P., Lorenzon, A., & Serpa, M. (2023). Challenges in high-performance computing. Journal of the Brazilian Computer Society, 29, 51–62. https://doi.org/10.5753/jbcs.2023.2219
Organisation for Economic Co-operation and Development. (2019). Artificial intelligence in society. OECD Publishing. https://doi.org/10.1787/eedfee77-en
Patino, C. M., & Ferreira, J. C. (2018). Inclusion and exclusion criteria in research studies: Definitions and why they matter. Jornal Brasileiro de Pneumologia, 44(2), 84. https://doi.org/10.1590/S1806-37562018000000088
Rangineni, S., Bhanushali, A., Suryadevara, M., Venkata, S., & Peddireddy, K. (2023). A review on enhancing data quality for optimal data analytics performance. International Journal of Computer Sciences and Engineering, 11, 51–58. https://doi.org/10.26438/ijcse/v11i10.5158
Ruan, M. (2024). The application of multimodal AI large model in the green supply chain of energy industry. Energy Informatics, 7, 98. https://doi.org/10.1186/s42162-024-00402-7
Santos, N. P. (2023). The expansion of data science: Dataset standardisation. Standards, 3(4), 400–410. https://doi.org/10.3390/standards3040028
Sharifi, A., & Mahdipour, H. (2024). Utilising geospatial artificial intelligence for remote sensing applications. Environmental Earth Sciences, 183, 274.
Silva, A., Schrum, M., Hedlund-Botti, E., Gopalan, N., & Gombolay, M. (2022). Explainable artificial intelligence: Evaluating the objective and subjective impacts of XAI on human-agent interaction. International Journal of Human–Computer Interaction, 39(2–3), 1–15. https://doi.org/10.1080/10447318.2022.2101698
Tanveer, M. H., Fatima, Z., Zardari, S., & Guerra-Zubiaga, D. (2023). An in-depth analysis of domain adaptation in computer and robotic vision. Applied Sciences, 13, 12823.
Wang, W., Xu, J., & Zhang, R. (2025). Optimised small object detection in low-resolution infrared images using super resolution and attention-based feature fusion. PLOS ONE, 20, e0328223. https://doi.org/10.1371/journal.pone.0328223
Xia, W., Yang, Y., & Xue, J.-H. (2020). Unsupervised multi-domain multimodal image-to-image translation with explicit domain constrained disentanglement. Neural Networks, 131, 50–63.
Xu, C., Du, X., Fan, X., Giuliani, G., Hu, Z., Wang, W., Liu, J., Wang, T., Yan, Z., Zhu, J., et al. (2022). Cloud-based storage and computing for remote sensing big data: A technical review. International Journal of Digital Earth, 15, 1417–1445.
Zhang, C., Xi, W., Liu, X., Bai, G., Sun, J., & Yu, F. (2022). Unsupervised multimodal image-to-image translation: Generate what you want. In Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN55064.2022.9892018
Zhang, C., Zhao, Q., & Wang, Y. (2020). Transferable attention networks for adversarial domain adaptation. Information Sciences, 539, 422–433.
Zhang, L., & Zhang, L. (2022). Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities. IEEE Geoscience and Remote Sensing Magazine, 10, 270–294.
Zhang, Z., Li, Q., Jing, W., He, G., Zhu, L., & Gao, S. (2024). Multimodal contrastive learning for remote sensing image feature extraction based on relaxed positive samples. Sensors, 24, 7719.
Zhang, Z., Zhang, M., Gong, J., Hu, X., Xiong, H., Zhou, H., & Cao, Z. (2023). LuoJiaAI: A cloud-based artificial intelligence platform for remote sensing image interpretation. Geo-Spatial Information Science, 26, 218–241.
Zhao, T., Wang, S., Ouyang, C., Chen, M., Liu, C., Zhang, J., Yu, L., Wang, F., Xie, Y., Li, J., Wang, F., Grunwald, S., Wong, B. M., Zhang, F., Qian, Z., Xu, Y., Yu, C., Han, W., Sun, T., … Wang, L. (2024). Artificial intelligence for geoscience: Progress, challenges, and perspectives. The Innovation, 5(5), 100691. https://doi.org/10.1016/j.xinn.2024.100691