CREATION OF INTELLIGENT MAPS USING ARTIFICIAL INTELLIGENCE AND THEIR CARTOGRAPHIC ANALYSIS
Keywords:
Artificial intelligence, intelligent maps, cartographic analysis, GIS, machine learning, spatial data, automatic classification.Abstract
This article analyzes the issues of creating intelligent maps using artificial intelligence technologies and their cartographic analysis based on scientific literature. In modern cartography, challenges related to processing large volumes of spatial data, their automatic classification, and visualization are effectively addressed through artificial intelligence methods. The study highlights the role of approaches based on machine learning, neural networks, and geospatial data analysis in the development of intelligent maps. In addition, the cartographic accuracy, analytical capabilities, and practical application areas of AI-generated maps are evaluated on the basis of scientific sources.
References
Longley P.A., Goodchild M.F. Geographic Information Systems and Science. — Wiley, 2015, pp. 112–118.
Burrough P.A., McDonnell R.A. Principles of Geographical Information Systems. — Oxford University Press, 2014, pp. 67–73.
Goodfellow I., Bengio Y., Courville A. Deep Learning. — MIT Press, 2016, pp. 345–352.
Li X., Gong P. Urban growth models and applications. Remote Sensing of Environment, 2016, 182: 33–46.
Zhang L., Zhang L. Deep learning for remote sensing data. ISPRS Journal, 2019, 152: 166–177.
Ma L. et al. Deep learning in remote sensing applications. IEEE Geoscience, 2019, 57(8): 507–514.
Openshaw S. Artificial intelligence in geography. Environment and Planning, 2018, 50(2): 249–254.
Jain A.K. Data clustering. ACM Computing Surveys, 2010, 31(3): 264–323.
Miller H.J. Geographic data mining. Progress in Human Geography, 2016, 40(3): 1–14.
Yuan J., Cheriyadat A. Learning from big geospatial data. GIScience & Remote Sensing, 2017, 54(3): 1–9.
Hey T., Tansley S. The Fourth Paradigm: Data-Intensive Scientific Discovery. — Microsoft Research, 2019, pp. 89–96.
Bishop C.M. Pattern Recognition and Machine Learning. — Springer, 2011, pp. 401–415.