The Reality of Artificial Intelligence Applications in Applied Geography in Libya
DOI:
https://doi.org/10.25130/jfa.conf.10.1.6Keywords:
Artificial Intelligence, Applied Geography, Geographic Information Systems (GIS)Abstract
This study seeks to explore the current state of artificial intelligence (AI) applications in the field of applied geography in Libya, within the context of the developmental and spatial challenges facing the country. It emerges from the pressing need to adopt modern technological tools capable of supporting decision-makers and directing spatial development toward greater efficiency and sustainability. The significance of the research lies in its treatment of an advanced intersection between geography—as an analytical and spatial science—and artificial intelligence—as a revolutionary technology that offers unprecedented capabilities in processing spatial data, monitoring geographic phenomena, and providing accurate predictive models across various domains such as urban planning, resource management, and environmental change tracking.
The study adopts a descriptive-analytical methodology, based on a review of relevant literature and expert interviews with professionals in artificial intelligence and geographic information systems. This approach aims to understand the depth of current AI applications and assess the readiness of Libyan institutions to adopt such advanced technologies.
The findings reveal that the use of AI in applied geography in Libya remains limited, mostly confined to individual research initiatives or academic projects, and has yet to be integrated into sustainable institutional frameworks or national policy-making structures.
Downloads
References
1. Al-Badri, M., & Miftah, A. (2022). Smart Traffic Monitoring Initiatives in Benghazi: Challenges and Opportunities. Libyan Journal of Applied Engineering, 7(1), 45–60.
2. Alhussein, N., El-Mahdi, K., & Salem, T. (2021). Using Remote Sensing to Monitor Vegetation Changes in the Green Mountain Region. Libyan Journal of Geography and Environmental Studies, 4(2), 88–104.
3. Angelidou, M., Psaltoglou, A., Komninos, N., Kakderi, C., & Tsarchopoulos, P. (2017). Enhancing sustainable urban development through smart city applications. Sustainable Cities and Society, 33, 31–42. https://doi.org/10.1016/j.scs.2017.05.016
4. Batty, M. (2013). The New Science of Cities. MIT Press.
5. Barcelona Smart City Report. (2021). Barcelona’s Smart Waste Management System: A Case Study. Barcelona City Council. https://smartcity.bcn.cat/en
6. Chesbrough, H. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press.
7. Goodchild, M. F. (2018). Reimagining the history of GIS. Annals of GIS, 24(1), 1–8. https://doi.org/10.1080/19475683.2018.1424734
8. Krause, A., Zimmermann, R., & Becker, C. (2020). Machine learning for smart agriculture: Predicting irrigation needs. Environmental Modelling & Software, 130, 104718. https://doi.org/10.1016/j.envsoft.2020.104718
9. Kroese, D., Becker, P., & Van Dijk, J. (2019). AI applications in flood prediction and water management: The Dutch approach. Journal of Urban Technology, 26(3), 44–59. https://doi.org/10.1080/10630732.2019.1580923
10. Li, X., Zhang, Y., Wang, X., & Liu, Q. (2018). Deep learning for vegetation classification using remote sensing data. Remote Sensing, 10(2), 203. https://doi.org/10.3390/rs10020203
11. Nam, T., & Pardo, T. A. (2011). Conceptualizing smart city with dimensions of technology, people, and institutions. Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, 282–291. https://doi.org/10.1145/2037556.2037602
12. Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
13. Smart Nation Singapore. (2020). Smart Nation: Empowering the Future. Government of Singapore. https://www.smartnation.gov.sg/
14. UN-Habitat. (2020). World Cities Report 2020: The Value of Sustainable Urbanization. United Nations Human Settlements Programme. https://unhabitat.org/wcr
15. Wang, J., Liu, R., & Zhao, H. (2020). AI-driven traffic management system: Case study of Chicago. Transportation Research Part C: Emerging Technologies, 112, 142–158. https://doi.org/10.1016/j.trc.2019.11.005
16. Zhou, Y., Chen, T., & Lin, Q. (2021). AI and Smart Transport Systems: A Case Study in Shanghai. IEEE Transactions on Intelligent Transportation Systems, 22(4), 2301–2312. https://doi.org/10.1109/TITS.2020.3001234