Data to Cartography New MDE-Based Approach for Urban Satellite Image Classification

Authors

  • Hafsa Ouchra

    Laboratory of Information Technology and Modeling LTIM, Faculty of Sciences Ben M’sik, Hassan II University, Casablanca, 20242, Morocco
  • Abdessamad Belangour

    Laboratory of Information Technology and Modeling LTIM, Faculty of Sciences Ben M’sik, Hassan II University, Casablanca, 20242, Morocco
  • Allae Erraissi

    Chouaib Doukkali University,24000, El Jadida, Morocco
  • Maria Labied

    Laboratory of Information Technology and Modeling LTIM, Faculty of Sciences Ben M’sik, Hassan II University, Casablanca, 20242, Morocco

DOI:

https://doi.org/10.30564/jees.v7i1.7054
Received: 15 August 2024 | Revised: 10 September 2024 | Accepted: 13 September 2024 | Published Online: 28 October 2024

Abstract

Monitoring of the earth's surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT, Landsat and Sentinel-2, which produce vast datasets. The processing of this data, often referred to as Big Data, is essential for decision-making, requiring the application of advanced algorithms to analyze changes in land cover. In the age of artificial intelligence, supervised machine learning algorithms are widely used, although their application in urban contexts remains complex. Researchers have to evaluate and tune various algorithms according to assumptions and experiments, which requires time and resources. This paper presents a meta-modeling approach for urban satellite image classification, using model-driven engineering techniques. The aim is to provide urban planners with standardized solutions for geospatial processing, promoting reusability and interoperability. Formalization includes the creation of a knowledge base and the modeling of processing chains to analyze land use.

Keywords:

Urban Geospatial Analysis; UrbanPlanningMeta-Models; ModelDrivenEngineering; MachineLearning;GeoAI

References

[1] Ouchra, H., Belangour, A., Erraissi, A., 2022. Satellite data analysis and geographic information system for urban planning: A systematic review. Proceedongs of the 2022 International Conference on Data Analytics for Business and Industry (ICDABI); Sakhir, Bahrain; 25–26 October 2022. pp. 558–564. DOI: https://doi.org/10.1109/ICDABI56818.2022.10041487

[2] Ouchra, H., Belangour, A., Erraissi, A., 2022. A comparative study on pixel-based classification and object-oriented classification of satellite image. International Journal of Engineering Trends and Technology. 70, 206–215. DOI: https://doi.org/10.14445/22315381/IJETT-V70I8P221

[3] Erraissi, A., Belangour, A., 2020. An approach based on model driven engineering for big data visualization in different visual modes. International journal of scientific & technology research. 9(1).

[4] Ouchra, H., Belangour, A., Erraissi, A., 2022. Spatial data mining technology for GIS: A review. Proceedongs of the 2022 International Conference on Data Analytics for Business and Industry (ICDABI); Sakhir, Bahrain; 25–26 October 2022. pp. 655–659. DOI: https://doi.org/10.1109/ICDABI56818.2022.10041574

[5] Ouchra, H., Belangour, A., Erraissi, A., 2023. An overview of Geospatial artificial intelligence technologies for city planning and development. Proceedongs of the 2023 5th International Conference on Electrical, Computer and Communication Technologies (ICECCT); Erode, India; 22–24 February 2023. pp. 1–7. DOI: https://doi.org/10.1109/ICECCT56650.2023.10179796

[6] Ouchra, H., Belangour, A., Erraissi, A., 2023. A comprehensive study of using remote sensing and geographical information systems for urban planning. Internetworking Indonesia Journal. 14(1), 15.

[7] Ouchra, H., Belangour, A., Erraissi, A., 2022. Machine learning for satellite image classification: A comprehensive review. Proceedongs of the 2022 International Conference on Data Analytics for Business and Industry (ICDABI); Sakhir, Bahrain; 25–26 October 2022. pp. 1–5. DOI: https://doi.org/10.1109/ICDABI56818.2022.10041606

[8] Ouchra, H., Belangour, A., 2021. Satellite image classification methods and techniques: A survey. Proceedings of the 2021 IEEE International Conference on Imaging Systems and Techniques (IST). Kaohsiung, Taiwan; 24–26 August 2021. pp. 1–6. DOI: https://doi.org/10.1109/IST50367.2021.9651454

[9] Ouchra, H., Belangour, A., Erraissi, A., 2023. Comparing unsupervised land use classification of landsat 8 OLI data using K-means and LVQ algorithms in Google Earth Engine: A case study of Casablanca. International Journal of Geoinformatics. 19(12), 83–92. DOI: https://doi.org/10.52939/ijg.v19i12.2981

[10] Ouchra, H., Belangour, A., Erraissi, A., 2024. Supervised machine learning algorithms for land cover classification in Casablanca, Morocco. Ingenierie des Systemes d’Information. 29(1), 377–387. DOI: https://doi.org/10.18280/ISI.290137

[11] Ouchra, H., Belangour, A., Erraissi, A., 2024. Unsupervised learning for land cover mapping of casablanca using multispectral imaging. Proceedings of the 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS); Manama, Bahrain; 28–29 January 2024. pp. 1841–1847. DOI: https://doi.org/10.1109/ICETSIS61505.2024.10459466

[12] Ouchra, H., Belangour, A., Erraissi, A., 2024. Exploring Google Earth Engine platform for satellite image classification using machine learning algorithms. Proceedings of the Innovations in Smart Cities Applications (SCA). ESTP; Paris, France; 4–6 March 2023. pp. 271–280. DOI: https://doi.org/10.1007/978-3-031-54376-0_24

[13] Ouchra, H., Belangour, A., Erraissi, A., 2023. Comparison of machine learning methods for satellite image classification: A case study of Casablanca using Landsat Imagery and Google Earth Engine. Journal of Environmental & Earth Sciences. 5(2), 118–134. DOI: https://doi.org/10.30564/JEES.V5I2.5928

[14] Ouchra, H., Belangour, A., Erraissi, A., 2023. Machine learning algorithms for satellite image classification using Google Earth Engine and landsat satellite data: Morocco case study. IEEE Access. 11, 71127–71142. DOI: https://doi.org/10.1109/ACCESS.2023.3293828

[15] Ouchra, H., Belangour, A., Erraissi, A., et al., 2024. Assessing machine learning algorithms for land use and land cover classification in Morocco using Google Earth Engine. Image Analysis and Processing - ICIAP 2023 Workshops; Torino, Italy; 11 September 2023. pp. 395–405. DOI: https://doi.org/10.1007/978-3-031-51023-6_33

[16] Razafinimaro, A., Hajalalaina, A.R., Reziky, Z.T., et al., 2021. Formalization of image processing chains for the dynamics forest cover using landsat satellite multi-sensor and multi-temporal. International Journal of Computer Trends and Technology. 69(2), 29–40. DOI: https://doi.org/10.14445/22312803/IJCTT-V69I2P105

[17] Razafinimaro, A., Hajalalaina, A.R., Rakotonirainy, H., et al., 2022. Novel approach for generalizing the process chain of optical satellite images based on knowledge capitalisation. International Journal of Geoinformatics. 18(6), 69–79. DOI: https://doi.org/10.52939/ijg.v18i6.2463

[18] Awad, M., 2021. Google Earth Engine (GEE) cloud computing based crop classification using radar, optical images and Support Vector Machine Algorithm (SVM). Proceedings of the 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology (IMCET); Beirut, Lebanon; 8–10 December 2021. pp. 71–76. DOI: https://doi.org/10.1109/IMCET53404.2021.9665519

[19] Li, D., Wang, S., Li, D., 2016. Spatial data mining: Theory and application. Springer Berlin/Heidelberg: Berlin, Germany. DOI: https://doi.org/10.1007/978-3-662-48538-5

[20] Borra, S., Thanki, R., Dey, N., 2019. Satellite image analysis: Clustering and classification. Springer: Singapore. p. 110.

[21] Natekin, A., Knoll, A., 2013. Gradient boosting machines, a tutorial. Front Neurorobot. 7. DOI: https://doi.org/10.3389/fnbot.2013.00021.

[22] Erraissi, A., Belangour, A., 2019. Meta-modeling of big data visualization layer using on-line analytical processing (OLAP). International Journal of Advanced Trends in Computer Science and Engineering. 8(4), 990–998. DOI: https://doi.org/10.30534/IJATCSE/2019/02842019

[23] Erraissi, A., Erraissi, A., Belangour, A., 2018. Data sources and ingestion big data layers: Meta-modeling of key concepts and features. International Journal of Engineering & Technology. 7(4), 3607–3612. DOI: https://doi.org/10.14419/ijet.v7i4.21742

[24] Hartmann, T., Moawad, A., Schockaert, C., et al., 2019. Meta-modelling meta-learning. Proceedings of the 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS); Munich, Germany; 15–20 September 2019. pp. 300–305. DOI: https://doi.org/10.1109/MODELS.2019.00014

[25] Ouchra, H., Belangour, A., 2021. Object detection approaches in images: A survey. Proceedings of SPIE - The International Society for Optical Engineering; Singapore, Singapore; 20–23 March 2021. DOI: https://doi.org/10.1117/12.2601452

[26] Ouchra, H., Belangour, A., 2021. Object detection approaches in images: A weighted scoring model based comparative study. International Journal of Advanced Computer Science and Applications. 12(8). DOI: https://doi.org/10.14569/IJACSA.2021.0120831

Downloads

How to Cite

Ouchra, H., Belangour, A., Erraissi, A., & Labied, M. (2025). Data to Cartography New MDE-Based Approach for Urban Satellite Image Classification. Journal of Environmental & Earth Sciences, 7(1), 18–28. https://doi.org/10.30564/jees.v7i1.7054

Issue

Vol. 7 , Iss. 1 (January 2025): In Progress

Article Type

Article

License

Copyright © 2024 Hafsa Ouchra, Abdessamad Belangour, Allae Erraissi, Maria Labied

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC