Citation :

Rajkumar N , 2025. "Artificial Intelligence for Remote Sensing Data Analysis: A Review of Challenges and Opportunities" ESP International Journal of Artificial Intelligence & Data Science [IJAIDS]  Volume 1, Issue 1: 27-36.

Abstract :

In a world full of data, remote sensing has become a powerful way to look at and learn about our globe. Remote sensing takes a lot of pictures and geographical data to keep an eye on things like deforestation and urban sprawl. But here's the problem: this data is often too big and complicated for people to look at quickly. That's when AI, like a superhero with a toolbox full of tricks, comes in. This paper goes into detail about how AI, specifically machine learning and deep learning, is changing the way we handle, understand, and use remote sensing data. It's about making machines smarter so they can help us see patterns, make predictions, and solve problems faster than ever. We are entering a new era of data analysis that is faster, more accurate, and can be scaled up indefinitely by combining traditional methods with modern AI. Remote sensing data has become one of the most important tools for observing and understanding the Earth on a large scale, but the data's complexity, volume, and speed make manual analysis both inefficient and insufficient. Artificial Intelligence (AI) is the game-changing force that is transforming the way remote sensing data is processed, understood, and used. AI, especially through machine learning and deep learning, lets you automatically classify, find objects, detect changes, and combine data from different sensors, like satellite images, LiDAR, hyperspectral, and multispectral data. AI improves precision agriculture, monitoring climate change, responding to disasters, mapping land use, and planning cities by using models that can learn from patterns, adapt to new inputs, and make decisions almost in real time. It also makes it possible to use time-series analysis to keep an eye on changes in the environment and make more accurate predictions about what will happen in the future. AI is changing the way remote sensing works in a big way, from edge computing on drones to cloud-based geospatial analytics. But there are problems that need to be dealt with properly, like small training datasets, model generalisation, explainability, and using data in an ethical way. As AI and geospatial technologies continue to come together, we are on the verge of a future where observing the Earth is more dynamic, intelligent, and useful than ever before.

References :

[1] Zhang, C., and Xie, Z. (2018). Mapping land cover using deep learning from high-resolution satellite pictures. The International Journal of Remote Sensing.

[2] Ma, L. et al. (2019). Machine learning in remote sensing: uses and problems. Remote Sensing.

[3] Anderson, D. T., Chan, C. S., and Ball, J. E. (2017). A full study of how deep learning is used in remote sensing. IEEE Magazine for Geoscience and Remote Sensing.

[4] Li, X., and Zhang, L. (2020). A look at how deep learning is used in remote sensing. The Journal of Remote Sensing

[5] Kamilaris and F. X. Prenafeta-Boldú (2018). A survey on deep learning in agriculture. Agricultural computers and electronics.

[6] X. X. Zhu et al. (2017). A look back at recent progress in deep learning for remote sensing. Letters from IEEE Geoscience and Remote Sensing.

[7] Chen, Y., Jiang, H., Li, C., Jia, X., and Ghamisi, P. (2016). Using CNNs to extract deep features and classify hyperspectral pictures. Transactions of the IEEE.

[8] Goodfellow, I., Bengio, Y., and Courville, A. (2016). Learning Deeply. Press from MIT.

[9] LeCun, Y., Bengio, Y., and Hinton, G. (2015). Learning deeply. Nature.

[10] N. Gorelick et al. (2017). Google Earth Engine lets anyone do geospatial analysis on a planetary scale. Remote Sensing of the Environment.

[11] A., Sukkarieh, S., and Whelan, B. (2018). Using machine learning to predict crop yields. Using computers and electronics in farming.

[12] Xu, Y., and Du, P. (2019). Remote sensing image scene classification: the best and the worst. Proceedings of the IEEE.Boulch, A., Le Saux, B., and Audebert, N. (2017). SnapNet: Using 2D deep segmentation networks to label 3D point clouds with meaning. Computers and Graphics.

[13] Belgiu, M. and Drăguţ, L. (2016). A look at how random forest is used in remote sensing and where it might go in the future. The ISPRS Journal.

[14] Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for cutting up biological images.

[15] F. Hu et al. (2015). Combining data from different types of sensors in remote sensing. The International Journal of Image and Data Fusion.

[16] Yuan, Q., Shen, H., and Zhang, L. (2016). Using many sensors to combine data in remote sensing. Sensors Journal.

[17] Ghamisi, P., Plaza, J., Chen, Y., Li, J., and Plaza, A. (2017). Advanced spectral classifiers for photos with several colours. IEEE Transactions.

[18] S. P. Mohanty, D. P. Hughes, and M. Salathé (2016). Using deep learning to find plant diseases in pictures. New things in plant science.

[19] Liu, D. and Xia, F. (2020). Edge AI for analysing distant sensing data in real time. Surveys of ACM Computing.

[20] Huang, X. and Zhang, L. (2012). Morphological building/shadow index for getting buildings out of high-resolution images. IEEE Journal.

[21] H. Zhang et al. (2021). A look at AI in the analysis of remote sensing scenes. Systems for computers, the environment, and cities.

[22] Audebert, N., Saux, B. L., and Lefèvre, S. (2018). Deep learning for perceiving things from far away in cities. IEEE Transactions.

[23] Y. Liu et al. (2018). Using deep learning to get roads out of high-resolution images. Remote Sensing.

[24] G. Sumbul and B. Demir (2020). BigEarthNet is a big collection of benchmark images for analysing remote sensing images. Journal of ISPRS.

[25] Lary, D. J., Alavi, A. H., Gandomi, A. H., and Walker, A. L. (2016). Using machine learning in geosciences and remote sensing. Geoscience Frontiers.

[26] Y. Zhang et al. (2021). A look from the outside at AI-powered disaster monitoring. Natural Dangers.

[27] Roy, D. P. et al. (2014). Landsat-8: A vision for science and products. Remote Sensing of the Environment

[28] Lin, T. Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017). Focal Loss for Finding Lots of Objects

[29] Ayrey, E., and Hayes, D. J. (2018). What LiDAR does for distant sensing in forests. Forest Management and Ecology

[30] C. Koparan et al. (2021). The future of smart farming is UAVs and AI. Journal of Precision Agriculture.

[31] Abdi, A. M. (2020). How well machine learning algorithms can classify land cover and land use. Monitoring and assessing the environment.

[32] Patel, D., Shah, V., and Sharma, P. (2022). AI for Earth Observation: Turning data into useful information. The Springer AI Series

[33] Wu, H., Zhang, C., and Xu, K. (2021). Deep learning techniques for combining satellite data from several sources. Remote Sensing.

[34] Li, S. et al. (2020). A look back at spatiotemporal fusion in remote sensing and what it could mean for the future. The ISPRS Journal.

[35] N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov (2017). Using deep learning to sort different types of land cover in satellite images. Journal of Information and Automation Sciences.

[36] F. Zhang et al. (2021). Real-time AI analysis on satellites: a new era for remote sensing. Astronomy in Nature.

[37] Knight, J. (2019). A appeal for openness in the ethics of AI in geospatial technology. The Journal of Spatial Science.

[38] M. Bosch et al. (2019). AI for scene classification: Problems with remote sensing datasets. Letters from Remote Sensing.

[39] Ayhan, B. and Tasdelen, E. (2022). Fairness and bias in AI algorithms for remote sensing. AI and Society.

[40] Ghosh, A. et al. (2023). Sustainable development goals and Earth monitoring powered by AI. Science and Policy for the Environment

[41] Singh, A. (1989). Using remotely-sensed data to find digital changes. The International Journal of Remote Sensing.

[42] Chen, G., and Hay, G. J. (2011). Change detection based on objects utilising high-resolution images. Remote Sensing of the Environment

[43] F. Doshi-Velez and B. Kim (2017). Towards a strict science of machine learning that can be understood. arXiv preprint.

[44] D. Rolnick et al. (2019). Using machine learning to fight climate change. Surveys of ACM Computing.

[45] C. M. Gevaert et al. (2018). Using AI and UAVs to map cities. The Journal of Urban Remote Sensing.

[46] V. Mnih and G. Hinton (2010). Learning how to find highways in high-resolution aerial photos.

[47] Rudd, E. M. et al. (2020). AI in mapping the whole planet: new ideas from the Mars rover missions. Science of the planets and space.

[48] Scott, G. J. et al. (2017). Using a small amount of data to train deep networks to classify land cover. Transactions of the IEEE.

[49] UN-SPIDER (2022). Best practices throughout the world for using AI and satellite data to respond to disasters. The United Nations Office for Outer Space Affairs.

Keywords :

Artificial Intelligence, Remote Sensing, Machine Learning, Deep Learning, Satellite Imagery, Change Detection, Land Use Classification, Hyperspectral Analysis, Data Fusion, Geospatial Intelligence, And Environmental Monitoring Are Some Of The Terms Used.