THE EVOLUTION AND IMPACT OF AI IN GEOGRAPHIC INFORMATION SYSTEMS
Keywords:
Artificial Intelligence (AI), Geographic Information Systems (GIS), Spatial Data Analysis, Machine Learning, Remote SensingAbstract
This article explores the transformative impact of Artificial Intelligence (AI) on Geographic Information Systems (GIS) over the past few decades. It traces the evolution of AI in GIS from its early applications in the 1990s, through its expanded role in predictive modeling, image classification, and spatial data mining, to its current state as an indispensable tool across various industries. The article highlights how AI has revolutionized spatial data analysis, improved decision-making processes, and enhanced our understanding of complex geographical phenomena. It presents numerical data on the improved accuracy, efficiency, and capabilities that AI brings to GIS applications in urban planning, disaster management, and environmental conservation. Finally, it looks at future prospects, discussing how advancements in machine learning, deep learning, and big data analytics are expected to further accelerate the synergy between AI and GIS, potentially leading to more precise spatial analyses and better solutions for complex global challenges.
References
MarketsandMarkets, "Geographic Information System (GIS) Market by Component (Hardware, Software, Services), Collection Medium (Terrestrial, Aerial, Satellite), Organization Size, Industry Vertical, and Region - Global Forecast to 2025," Nov. 2020. [Online]. Available: https://www.marketsandmarkets.com/Market-Reports/geographic-information-system-market-55818275.html
S. Shekhar, Z. Jiang, R. Y. Ali, E. Eftelioglu, X. Tang, S. Gunturi, and X. Zhou, "Spatiotemporal Data Mining: A Computational Perspective," ISPRS International Journal of Geo-Information, vol. 4, no. 4, pp. 2306–2338, Oct. 2015. [Online]. Available: https://doi.org/10.3390/ijgi4042306
R. F. Tomlinson, "Thinking About GIS: Geographic Information System Planning for Managers," ESRI Press, 1998. [Online]. Available: https://www.e-education.psu.edu/geog583/sites/www.e-education.psu.edu.geog583/files/Thinking%20About%20GIS-Tomlinson.pdf
P. M. Atkinson and A. R. L. Tatnall, "Introduction Neural networks in remote sensing," International Journal of Remote Sensing, vol. 18, no. 4, pp. 699-709, 1997. [Online]. Available: https://doi.org/10.1080/014311697218700
S. Li,, "Geospatial big data handling theory and methods: A review and research challenges," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 115, pp. 119-133, 2016. [Online]. Available: https://doi.org/10.1016/j.isprsjprs.2015.10.012
X. X. Zhu,, "Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources," IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 4, pp. 8-36, 2017. [Online]. Available: https://doi.org/10.1109/MGRS.2017.2762307
M. Reichstein, "Deep learning and process understanding for data-driven Earth system science," Nature, vol. 566, no. 7743, pp. 195-204, 2019. [Online]. Available: https://doi.org/10.1038/s41586-019-0912-1
L. Ma, Y. Liu, X. Zhang, Y. Ye, G. Yin, and B. A. Johnson, "Deep learning in remote sensing applications: A meta-analysis and
review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 152, pp. 166-177, 2019. [Online]. Available: https://doi.org/10.1016/j.isprsjprs.2019.04.015
S. Wang, W. Chen, S. M. Xie, G. Azzari, and D. B. Lobell, "Weakly supervised deep learning for segmentation of remote sensing imagery," Remote Sensing of Environment, vol. 252, p. 112046, 2021. [Online]. Available: https://www.mdpi.com/2072-4292/12/2/207
