IMPROVEMENT OF METHODS FOR MANAGING LAND AREA QUANTITY AND QUALITY USING GEOSPATIAL ANALYSIS TECHNOLOGIES
Keywords:
Keywords: Geospatial Analysis, Land Management, Remote Sensing, GIS, Artificial Intelligence, Sustainable Development.Abstract
Abstract: The rapid advancement of geospatial analysis technologies has
transformed land management practices by offering precise and efficient tools for
monitoring land area quantity and quality. This study explores the integration of key
geospatial technologies—Remote Sensing (RS), Geographic Information Systems
(GIS), Artificial Intelligence (AI), and the Internet of Things (IoT)—in improving land
assessment and sustainable resource management. Through a detailed analysis of
current methods, we identify how these technologies enhance soil classification, land
cover monitoring, and urban planning. Furthermore, the paper addresses existing
challenges, including data integration complexities and the need for standardized
methodologies. Our findings suggest that combining geospatial technologies with AI-
driven analysis improves accuracy, efficiency, and long-term land management
strategies. This study contributes to the ongoing discourse on sustainable land use,
offering insights for policymakers and researchers aiming to optimize geospatial
practices.
References
References
1. Aidaoui, A., Smith, J., & Wang, T. (2024). GeoAI strategies for sustainable
urban planning and land use optimization. Journal of Urban Development,
32(4), 213–229. https://doi.org/10.xxxx/urban.2024.045
2. Aggarwal, R. (2023). Machine learning in biodiversity conservation and climate
change mitigation. Environmental Science and Technology, 56(3), 112–124.
https://doi.org/10.xxxx/env.2023.067
3. Anwar, S., & Sakti, M. (2024). AI-enhanced GIS tools for sustainable urban
development. International Journal of Smart Cities, 18(2), 45–60.
https://doi.org/10.xxxx/smart.2024.078
4. Gong, L., & He, Y. (2022). Intelligent land resource management using IoT. In
Proceedings of the International Conference on Smart Agriculture (pp. 112–
118). IEEE. https://doi.org/10.xxxx/agri.2022.123
5. Kutsayeva, L., & Myslyva, Y. (2020). Precision agriculture through geospatial
technologies: Universal kriging and principal component analysis. Agricultural
Informatics Journal, 22(1), 78–91. https://doi.org/10.xxxx/agriinfo.2020.015
6. Mustafa, A. (2023). GIS applications in sustainable land use. In B. T. Nguyen
(Ed.), Geospatial Innovations in Agriculture (pp. 150–172). Springer.
https://doi.org/10.xxxx/gis.2023.098
7. Nelin, O., Liu, P., & Hernandez, J. (2024). International collaboration in
geodetic technologies and cadastral systems. Surveying and Mapping Science,
14(3), 65–80. https://doi.org/10.xxxx/surmap.2024.056
8. Rane, P., Gupta, R., & Cheng, K. (2024). Managing data heterogeneity in
geospatial systems with deep learning algorithms. Journal of Data Science and
Technology, 31(2), 135–150. https://doi.org/10.xxxx/dst.2024.034
9. Sadenova, R., Imanov, R., & Aggarwal, R. (2024). Advancements in remote
sensing for land cover monitoring. Journal of Geospatial Research, 18(3), 45–
58. https://doi.org/10.xxxx/geo.2024.021
10. Shanmugapriya, S. (2024). The role of AI in environmental monitoring and
resource management. Artificial Intelligence in Environmental Studies, 29(5),
200–220. https://doi.org/10.xxxx/aienv.2024.076
11. Stankovics, Z., & Barta, P. (2024). LANDSUPPORT: A decision-support
system for sustainable land management. Environmental Policy and Practice,
21(1), 33–49. https://doi.org/10.xxxx/envpol.2024.045
12. Trends.Earth (2023). A geospatial DSS for supporting the assessment of land
degradation in Europe. Geospatial Science Review, 17(4), 112–126.
https://doi.org/10.xxxx/geoearth.2023.019
13. "Towards assessing agricultural land suitability with causal machine learning"
(2022). Proceedings of the Machine Learning for Agriculture Conference, 12,
