Comparison of Machine Learning Land Use-Land Cover Supervised Classifiers Performance on Satellite Imagery Sentinel 2 using Lazy Predict Library
Abstract
The utilisation of various supervised classifier algorithms in classifying land use and land cover (LULC) from satellite imagery has been widely used worldwide, yet the implementation using lazy predict library remained unexplored. This study aims to create the LULC supervised classifier model for Sentinel 2 satellite images using lazy predict library and assess its capability for creating multiple machine learning models. The result of this study shows that lazy predict library can generate 26 machine learning models in efficient few lines of code and less time-consuming. Most LULC models generated by lazy predicts has performance metrics above 90% with time computation between 0 and 1 seconds. While lazy predict library has benefits to generate various machine learning models at once, it has drawbacks in terms of its feasibility for the machine learning production, its obstacle running in local environment, and its requirements for the RAM computation.
Downloads
References
E. Alshdaifat , D. Alshdaifat, A. Alsarhan, F. Hussein and S. M. F. El Salhi, "The Effect of Preprocessing Techniques, Applied to Numeric Features, on Classification Algorithms’ Performance," Data, vol. 6, no. 2, p. 11, 2021. https://doi.org/10.3390/data6020011
P. Manikandan and D. Ramyachitra, "Naïve Bayes Classification Technique for Analysis of Ecoli Imbalance Dataset," International Journal of Computational Intelligence and Informatics, vol. 4, no. 2, pp. 98-103, 2014. https://www.periyaruniversity.ac.in/ijcii/issue/Vol4No2September2014/IJCII-4-2-141.pdf
J. Han, M. Kamber and J. Pei, 2012, Waltham: Morgan Kaufmann, Data Mining: Concepts and Techniques. https://doi.org/10.1016/C2009-0-61819-5
A. Jamali, "Land Use Land Cover Mapping Using Advanced Machine Learning Classifiers," Ekológia (Bratislava), vol. 40, no. 3, pp. 286-300, 2020. https://doi.org/10.2478/eko-2021-0031
A. Mahmood, Y. Sandali and . J.-L. Wang, "Easy and Fast Prediction of Green Solvents for Small Molecule Donor-Based Organis Solar Cells Through Machine Learning," Physical Chemistry Chemical Physics, vol. 25, no. 15, pp. 10417-10426, 2023. https://doi.org/10.1039/D3CP00177F
H. J. Escalante, "A Comparison of Outlier Detection Algorithms for Machine Learning," Programming and Computer Software, pp. 228-237, 2005. https://www.researchgate.net/publication/228728521
T. K. Nguyen, T. N. L. Nguyen, K. Nguyen, H. V. T. Nguyen, L. T. T. Tran, T. X. T. Ngo, P. T. V. Pham and M. H. Tran, "Machine Learning-Based Screening of MCF-7 Human Breast Cancer Cells and Molecular Docking Analysis of Essential Oils from Ocimum Basilicum Against Breast Cancer," Journal of Molecular Structure, vol. 1268, no. 133627, 2022. https://doi.org/10.1016/j.molstruc.2022.133627
O. Y. Ouma, A. Keitsile, B. Nkwae, P. Odirile, D. Moalafhi and J. Qi, "Urban Land-Use Classification Using Machine Learning Classifiers: Comparative Evaluation and Post-Classification Multi-Feature Fusion Approach," European Journal of Remote Sensing, vol. 56, no. 1, p. 2173659, 2023. https://doi.org/10.1080/22797254.2023.2173659
A. E. E. Rashed, A. M. Elmorsy and A. E. M. Atwa, "Comparative Evaluation of Automated Machine Learning Techniques for Breast Cancer Diagnosis," Biomedical Signal Processing and Control, vol. 86, p. 105016, 2023. https://doi.org/10.1016/j.bspc.2023.105016
S. Swetanisha, A. R. Panda and D. K. Behera, "Land Use/Land Cover Classification Using Machine Learning," International Journal of Electrical and Computer Engineering (IJECE), vol. 12, no. 2, pp. 2040-2046, 2022. http://doi.org/10.11591/ijece.v12i2.pp2040-2046
S. Talukdar, P. Singha, S. Mahato, Shahfahad, S. Pal, Y.-A. Liou and A. Rahman, "Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Oberservations—A Review," Remote Sens, vol. 12, no. 7, p. 1135, 2020. https://doi.org/10.3390/rs12071135
A. Tharwat, "Classification Assessment Methods," Applied Computing and Informatics, vol. 17, no. 1, pp. 168-192, 2021. https://doi.org/10.1016/j.aci.2018.08.003
Y. G. Yuh, W. Tracz, H. D. Matthews and S. E. Turner, "Application of Machine Learning Approaches for Land Cover Monitoring in Northern Cameroon," Ecological Informatics, vol. 74, p. 101955, 2023. https://doi.org/10.1016/j.ecoinf.2022.101955
S. Aldiansyah and R. A. Saputra, "Comparison Of Machine Learning Algorithms for Land Use and Land Cover Analysis Using Google Earth Engine (Case Study: Wanggu Watershed)," International Journal of Remote Sensing and Earth Sciences, vol. 19, no. 2, pp. 197-210, 2023. https://doi.org/10.30536/j.ijreses.2022.v19.a3803
S. Basheer, X. Wang, A. A. Farooque, R. A. Nawaz, K. Liu, T. Adekanmbi and S. Liu, "Comparison of Land Use Land Cover Classifiers Using," Remote Sens, vol. 14, no. 19, p. 4978, 2022. https://doi.org/10.3390/rs14194978
Copyright (c) 2024 Indonesian Journal of Data and Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- The work is not under consideration for publication elsewhere.
- The work has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Indonesian Journal of Data and Science agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. (CC BY-NC 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.