Digital processing of thematic maps using artificial intelligence technologies
Article Sidebar
Main Article Content
Abstract
The study seeks to address and improve the clarity of some thematic maps that suffer from poor spatial accuracy, by using some artificial intelligence techniques, represented by (GIGA PIXEL AI) technology to increase the number of map pixels and the Python language (OpenCV) to improve the quality of the map, in order to achieve spatial accuracy. High and achieving more effective results in the process of analyzing, interpreting and reading the map. A group of thematic maps published in Iraqi academic journals was selected, and some automated processing was performed on them, in order to improve their spatial accuracy. (4) maps were selected to conduct the processing operations, and data was extracted. pixels using mindonmap library,
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
- Asmaa Zaid Al-Thuwaini, Fouad Abdel-Wahab Al-Omari, the role of geomorphological processes in shaping the slopes of the northern Hamrin anticline between Al-Fatha and the Kirkuk Road, published research, Tikrit University Journal of Human Sciences, Volume 31, Issue No. (4), 2024, p. 256.
- Najeeb Abdul Rahman Al-Zaidi, Saad Thamer Ibrahim Al-Hamdani, Quantitative Thematic Maps “Problems and Solutions:, 1st edition, Dar Al-Ebdaa for Printing and Publishing, Iraq, 2020, p. 19.
- Ammar Abdul Rahim, Building statistical analysis maps using geographic information systems for primary schools in the city of Al-Zubair, published research, Maysan Journal of Academic Studies, Volume 19, Issue 39, 2020, p. 55.
- Wissam Ali Kabtan, Determining geographical distribution trends in the city of Baqubah for the period (1987-2010), published research, Diyala Journal for Humanitarian Research, Volume, Issue 61, 2014, p. 553.
- Najeeb Abdul Rahman Mahmoud Al-Zaidi, Ahmed Muhammad Jihad Al-Kubaisi, Geospatial Artificial Intelligence, 1st edition, Dara Al-Ebdaa, 2023, p. 38.
- Drăguț, L., & Constantin, D. (2020). Artificial Intelligence and the Advancement of Geography: State of the Art and Future Perspectives. Journal of Settlements and Spatial Planning, 11(1), 61-70.
- Chen, Z., Zhang, L., & Xu, L. (2019). Object-based land-use change detection using machine learning algorithms and Landsat imagery. Remote Sensing, 11(2), 186.
- Mitchell, Tom M. Machine learning. McGraw-Hill Higher Education, 1997,p56.
- Boulton, C., & Leszczynski, A. (2019). Applications of artificial neural networks in geographical research: A systematic review. International Journal of Geographical Information Science, 33(5), 943-963.
- Arsanjani, J. J., Helbich, M., Bakillah, M., & Zipf, A. (2015). Towards mapping land-use patterns from volunteered geographic information with artificial neural networks. International Journal of Geographical Information Science, 29(12), 2264-2278.
- Goodchild, M. F. (2020). Twenty years of progress: GIScience in 2020. Journal of Spatial Information Science, 21, 3-20.
-LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.
- Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59.
-Smith, J., & Johnson, A. (2021). Geospatial Artificial Intelligence Technology and Applications. International Journal of Geographic Information Science, 35(6), 1079-1101.