Perfecting the accuracy of digital map production from satellite imagery using application geomatics techniques

• Autorzy: Jasim Basheer S. , AL-Asadi Mohammed A.

Identyfikatory

DOI

10.12912/27197050/196526

• Języki publikacji: EN

Abstrakty

EN

Challenges remain in data retrieval and mapping development when compared to other land objects, despite the dependability of remote sensing technology in assessing land use and land cover distribution. The fuzzy ARTMAP model is an ART-based neural network (FAM). The goal of this study is to use satellite imagery and advanced geomatics methods to create accurate digital maps. Field coordinates were matched with satellite imagery to increase spatial accuracy as part of a series of geomatic correction processes that also included geographic correction using GPS coordinates. The fuzzy ARTMAP method was used to assess the quality of the data classification. This algorithm has already shown its efficacy in distinguishing between farmlands, urban structures, and arid lands. The algorithm’s kappa value of 0.83 and overall accuracy of 89% indicate a very reliable data classification process. Further, extensive evaluations of the accuracy of geographical measures were carried out, specifically focussing on areas and distances. The findings indicated an overall error of 0.73% for distances and a mere 0.03% for areas. These results indicate that the methods used to get very high degrees of spatial accuracy while simultaneously decreasing spatial deviations work. The findings show that state-of-the-art georectification methods coupled with current classification algorithms may significantly enhance digital map quality, making them more reliable for applications such as environmental change monitoring, urban planning, and natural resource management. The research reinforces the importance of integrating low- and medium-resolution satellite imagery with modern geomatics techniques to achieve high-resolution digital maps.

Słowa kluczowe

EN

georectification; spatial deviations; natural resource management; monitoring; land use

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2025
• Tom: Vol. 26, iss. 2
• Strony: 45--54
• Opis fizyczny: Bibliogr 19 poz., rys., tab.

Twórcy

autor

Jasim Basheer S.

• Technical Institute of Babylon, Al-Furat Al-Awsat Technical University, Iraq

• https://orcid.org/0000-0001-6768-1451

autor

AL-Asadi Mohammed A.

• Technical Institute of Babylon, Al-Furat Al-Awsat Technical University, Iraq

• https://orcid.org/0009-0003-8665-8099

Bibliografia

• 1. Al-Hameedawi, A. N. M. (2022). Fuzzy logic approach based on geomatics and remote sensing for siting a petroleum warehouse in the metropolitan area of Baghdad. Journal of the Indian Society of Remote Sensing, 50(7), 1211–1225.
• 2. Al-Saedi, A. S. J., Kadhum, Z. M., & Jasim, B. S. (2023). Land Use and land cover analysis using geomatics techniques in Amara City. Ecol. Eng, 9, 161–169.
• 3. Backes, D. J., & Teferle, F. N. (2020). Multiscale integration of high-resolution spaceborne and
• drone-based imagery for a high-accuracy digital elevation model over Tristan da Cunha. Frontiers in Earth Science, 8, 319.
• 4. Carpenter, G. A., Grossberg, S., Markuzon, N., & Reynolds, J. H. (1991). Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. Boston University Center for Adaptive Systems and Department of Cognitive ….
• 5. Jasim, B., Jasim, O. Z., & AL-Hameedawi, A. N. (2024). Monitoring change detection of vegetation vulnerability using hotspots analysis. IIUM Engineering Journal, 25(2), 116–129.
• 6. Jasim, B. S., Al-Saedi, A. S. J., & Kadhum, Z. M. (2024). Using remote sensing application for verification of thematic maps produced based on high-resolution satellite images. AIP Conference Proceedings, 3092(1).
• 7. Jasim, B. S., Jasim, O. Z., & AL-Hameedawi, A. N. (2024). A review for vegetation vulnerability using artificial intelligent (AI) techniques. AIP Conference Proceedings, 3092(1).
• 8. Kadhum, Z. M., Jasim, B. S., & Al-Saedi, A. S. J. (2023). Improving the spectral and spatial resolution of satellite image using geomatics techniques. AIP Conference Proceedings, 2776(1).
• 9. Langat, P. K., Kumar, L., Koech, R., & Ghosh, M. K. (2021). Monitoring of land use/land-cover dynamics using remote sensing: a case of Tana River Basin, Kenya. Geocarto International, 36(13), 1470–1488.
• 10. Latifovic, R., & Olthof, I. (2004). Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data. Remote Sensing of Environment, 90(2), 153–165.
• 11. Lerner, B., & Guterman, H. (2008). Advanced developments and applications of the fuzzy ARTMAP neural network in pattern classification. In Computational Intelligence Paradigms: Innovative Applications 77–107. Springer.
• 12. Leta, M. K., Demissie, T. A., & Tränckner, J. (2021). Modeling and prediction of land use land cover change dynamics based on land change modeler (Lcm) in nashe watershed, upper blue nile basin, Ethiopia. Sustainability, 13(7), 3740.
• 13. Matias, A. L. S., Neto, A. R. R., Mattos, C. L. C., & Gomes, J. P. P. (2021). A novel fuzzy ARTMAP with area of influence. Neurocomputing, 432, 80–90.
• 14. Nandika, M. R., Ulfa, A., Ibrahim, A., & Purwanto, A. D. (2023). Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms. 17(2), 69–87.
• 15. Pal, S., & Talukdar, S. (2020). Assessing the role of hydrological modifications on land use/land cover dynamics in Punarbhaba river basin of Indo-Bangladesh. Environment, Development and Sustainability, 22, 363–382.
• 16. Systems, N. (1991). Fuzzy ARTMAP : A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps.
• 17. Vrigazova, B. (2021). The proportion for splitting data into training and test set for the bootstrap in classification problems. Business Systems Research: International Journal of the Society for Advancing Innovation and Research in Economy, 12(1), 228–242.
• 18. Yang, C., Wu, G., Ding, K., Shi, T., Li, Q., & Wang, J. (2017). Improving land use/land cover classification by integrating pixel unmixing and decision tree methods. Remote Sensing, 9(12), 1222.
• 19. Zabihi, M., Moradi, H., Gholamalifard, M., Khaledi Darvishan, A., & Fürst, C. (2020). Landscape management through change processes monitoring in Iran. Sustainability, 12(5), 1753.