DOI: https://doi.org/10.14710/geoplanning.12.2.253-266

Spatial Classification of Sentinel-2 Satellite Images with Machine Learning Approach

Dea Ratu Nursidah  -  Statistics Department, Faculty of Mathematics and Natural Science, Universitas Islam Indonesia, Indonesia., Indonesia
*Achmad Fauzan orcid scopus  -  Universitas Islam Indonesia, Indonesia
Marcelinus Alfafisurya Setya Adhiwibawa  -  Agricultural Data System Scientist, PCTC, Mondelez International, Indonesia

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Abstract

Urban expansion and land use change are increasingly critical issues in developing regions, where rapid development often leads to unplanned growth and environmental challenges. Accurate and timely classification of built-up and non-built-up areas is essential for supporting sustainable spatial planning and resource management. This study aims to classify built-up and non-built-up areas from Sentinel-2 satellite imagery using a machine learning approach and to analyze their spatial distribution around the Universitas Islam Indonesia (UII) campus. Three machine learning algorithms—Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT)—were applied to perform the classification, and their performances were evaluated using four metrics: accuracy, sensitivity, specificity, and Area Under the Curve (AUC). Among these, the SVM method demonstrated the best performance based on the highest average accuracy, the smallest variance difference between training and testing datasets, and consistent results across multiple iterations. Using the classification results from the best-performing model, a spatial density proportion analysis was conducted. The findings revealed a clear spatial trend: areas closer to the UII campus exhibited a higher proportion of built-up land, while areas located farther from the center had a greater share of non-built-up land. These results confirm the effectiveness of the SVM algorithm for land cover classification using Sentinel-2 imagery and offer valuable insights into urban development patterns in the study area. The outcomes of this research can inform urban planners and policymakers in developing data-driven strategies for sustainable land use, infrastructure development, and campus-centered regional growth planning.

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Keywords: Satellite Imagery; Classification; Machine Learning

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Section: Original Research
Language : EN
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  1. Abdullah, A. Z., Winarno, B., & Saputro, D. R. S. (2021). The Decision Tree Classification with C4.5 and C5.0 Algorithm based on R to Detect Case Fatality Rate of Dengue Hemorrhagic Fever in Indonesia. Journal of Physics: Conference Series, 1776(1), 012040. https://doi.org/10.1088/1742-6596/1776/1/012040">[Croosref]

  2. Abebe, G., Getachew, D., & Ewunetu, A. (2022). Analysing Land Use/Land Cover Changes and Its Dynamics Using Remote Sensing and GIS in Gubalafito District, Northeastern Ethiopia. SN Applied Sciences, 4(1), 30. https://doi.org/10.1007/s42452-021-04915-8">[Crossref]

  3. Ahmad, W., Iqbal, J., Nasir, M. J., Ahmad, B., Khan, M. T., Khan, S. N., & Adnan, S. (2021). Impact of Land use/Land Cover Changes on Water Quality and Human Health in District Peshawar Pakistan. Scientific Reports, 11(1), 16526. https://doi.org/10.1038/s41598-021-96075-3">[Crossref]

  4. Ahmed, Z., Krupnik, T. J., & Kamal, M. (2018). Introduction to Basic GIS and Spatial Analysis Using QGIS: Applications in Bangladesh. Dhaka, CIMMYT--Bangladesh, 80–94.

  5. Athanasiou, L. S., Fotiadis, D. I., & Michalis, L. K. (2017). Propagation of Segmentation and Imaging System Errors. In Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging (pp. 151–166). Elsevier. https://doi.org/10.1016/B978-0-12-804734-7.00008-7">[Crossref]

  6. Awad, M., & Khanna, R. (2015). Support Vector Machines for Classification. In Efficient Learning Machines (pp. 39–66). Apress. https://doi.org/10.1007/978-1-4302-5990-9_3">[Crossref]

  7. Baig, M. F., Mustafa, M. R. U., Baig, I., Takaijudin, H. B., & Zeshan, M. T. (2022). Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Selangor, Malaysia. Water, 14(3), 402. https://doi.org/10.3390/w14030402">[Crossref]

  8. Basheer, S., Wang, X., Farooque, A. A., Nawaz, R. A., Liu, K., Adekanmbi, T., & Liu, S. (2022). Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques. Remote Sensing, 14(19), 4978. https://doi.org/10.3390/rs14194978">[Crossref]

  9. Bettinger, P., Boston, K., Siry, J. P., & Grebner, D. L. (2017). Geographic Information and Land Classification in Support of Forest Planning. In Forest Management and Planning (pp. 65–85). Elsevier. https://doi.org/10.1016/B978-0-12-809476-1.00003-5">[Crossref]

  10. Bruy, A., & Svidzinska, D. (2015). QGIS by Example. Packt Publishing Ltd.

  11. Çalışkan, E. B. (2023). Tracking the University Campus Development by Using Remote Sensing and Satellite Imagery. Gazi University Journal of Science Part B: Art Humanities Design and Planning, 11(2), 237–252.

  12. Costa, V. G., & Pedreira, C. E. (2023). Recent Advances in Decision Trees: An Updated Survey. Artificial Intelligence Review, 56(5), 4765–4800. https://doi.org/10.1007/s10462-022-10275-5">[Crossref]

  13. Cunningham, M. A. (2006). Accuracy Assessment of Digitized and Classified Land Cover Data for Wildlife Habitat. Landscape and Urban Planning, 78(3), 217–228.https://doi.org/10.1016/j.landurbplan.2005.08.002">[Crossref]

  14. de Diego, I. M., Redondo, A. R., Fernández, R. R., Navarro, J., & Moguerza, J. M. (2022). General Performance Score for Classification Problems. Applied Intelligence, 52(10), 12049–12063. https://doi.org/10.1007/s10489-021-03041-7">[Crossref]

  15. Dharmawan, I. A., Rahadianto, M. A. E., Henry, E., Endyana, C., & Aufaristama, M. (2021). Application of High-Resolution Remote-Sensing Data for Land Use Land Cover Mapping of University Campus. The Scientific World Journal, 2021, 1–17. https://doi.org/10.1155/2021/5519011">[Crossref]

  16. Egwom, O. J., Hassan, M., Tanimu, J. J., Hamada, M., & Ogar, O. M. (2022). An LDA–SVM Machine Learning Model for Breast Cancer Classification. BioMedInformatics, 2(3), 345–358. https://doi.org/10.3390/biomedinformatics2030022">[Crossref]

  17. Fitzmaurice, G. M. (2016). Regression. Diagnostic Histopathology, 22(7), 271–278. https://doi.org/10.1016/j.mpdhp.2016.06.004">[Crossref]

  18. Flenniken, J. M., Stuglik, S., & Iannone, B. V. (2020). Quantum GIS (QGIS): An Introduction to a Free Alternative to More Costly GIS Platforms. EDIS, 2020(2), 7. https://doi.org/10.32473/edis-fr428-2020">[Crossref]

  19. Giorgi, F. M., Ceraolo, C., & Mercatelli, D. (2022). The R Language: An Engine for Bioinformatics and Data Science. Life, 12(5), 648. https://doi.org/10.3390/life12050648">[Crossref]

  20. Girma, R., Fürst, C., & Moges, A. (2022). Land Use Land Cover Change Modeling by Integrating Artificial Neural Network with Cellular Automata-Markov Chain Model in Gidabo River Basin, Main Ethiopian Rift. Environmental Challenges, 6, 100419. https://doi.org/10.1016/j.envc.2021.100419">[Crossref] 

  21. Government of the Republic of Indonesia (2002) Law No. 28 of 2002 on Buildings. Indonesia

  22. Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X., & Zhang, X. (2015). Impacts of Urban Biophysical Composition on Land Surface Temperature in Urban Heat Island Clusters. Landscape and Urban Planning, 135, 1–10. https://doi.org/10.1016/j.landurbplan.2014.11.007">[Crossref]

  23. Hagenaars, J. A. P., Kühnel, S., & Andress, H.-J. (2024). Interpreting and Comparing Effects in Logistic,Probit, and Logit Regression (Issue 194). Sage Publications.

  24. Huang, H., Sun, G., Ren, J., Rang, J., Zhang, A., & Hao, Y. (2018). Spectral-Spatial Topographic Shadow Detection from Sentinel-2A MSI Imagery Via Convolutional Neural Networks. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 661–664. https://doi.org/10.1109/IGARSS.2018.8517956">[Crossref]

  25. Iranmanesh, M., Seyedabrishami, S., & Moridpour, S. (2022). Identifying High Crash Risk Segments in Rural Roads Using Ensemble Decision Tree-based Models. Scientific Reports, 12(1), 20024. https://doi.org/10.1038/s41598-022-24476-z">[Crossref]

  26. Janipella, R., Gupta, V., & Moharir, R. V. (2019). Application of Geographic Information System in Energy Utilization. In Current Developments in Biotechnology and Bioengineering (pp. 143–161). Elsevier. https://doi.org/10.1016/B978-0-444-64083-3.00008-7">[Crossref]

  27. Kaur, R., & Pandey, P. (2022). A Review on Spectral Indices for Built-Up Area Extraction Using Remote Sensing Technology. Arabian Journal of Geosciences, 15(5), 391. https://doi.org/10.1007/s12517-022-09688-x">[Crossref]

  28. Kulkarni, K., & Vijaya, P. (2021). NDBI Based Prediction of Land Use Land Cover Change. Journal of the Indian Society of Remote Sensing, 49(10), 2523–2537. https://doi.org/10.1007/s12524-021-01411-9">[Crossref]

  29. Latuamury, B., Imlabla, W. N., Sahusilawane, F., & Marasabessy, H. (2025). Spatial Patterns of NDVI Vegetation Greenness Index and NDWI Water Content Index Using Sentinel 2A Imagery. IOP Conference Series: Earth and Environmental Science, 1527(1), 012002. https://doi.org/10.1088/1755-1315/1527/1/012002">[Crossref]

  30. Li, X. (2017). Buffers. Geographic Information Science & Technology Body of Knowledge, 2017(Q4). https://doi.org/10.22224/gistbok/2017.4.10">[Crossref]

  31. Listiana, E., Muzayanah, R., Muslim, M. A., & Sugiharti, E. (2023). Optimization of Support Vector Machine Using Information Gain and AdaBoost to Improve Accuracy of Chronic Kidney Disease Diagnosis. Journal of Soft Computing Exploration, 4(3), 152–158. https://doi.org/10.52465/joscex.v4i3.218">[Crossref]

  32. Löwe, P., Anguix Alfaro, Á., Antonello, A., Baumann, P., Carrera, M., Durante, K., Hugentobler, M., Lime, S., Mitasova, H., Müller, D., Neteler, M., Reed, J., Strobl, C., & Wessel, P. (2022). Open Source – GIS (pp. 807–843). https://doi.org/10.1007/978-3-030-53125-6_30">[Crossref]

  33. Nugroho, A. S., Witarto, A. B., & Handoko, D. (2003). Support Vector Machine. Proceeding Indones. Sci. Meeiting Cent. Japan.

  34. Pavlenko, L., Pavlenko, M., Khomenko, V., & Mezhuyev, V. (2020). Application of R Programming Language in Learning Statistics. Proceedings of the 1st Symposium on Advances in Educational Technology, 62–72. https://doi.org/10.5220/0010928500003364">[Crossref]

  35. Pebesma, E., & Bivand, R. (2023). Spatial Data Science: With Applications in R. Chapman and Hall/CRC.

  36. Priyatna, R. D., Tulus, & Ramli, M. (2018). K-Means Algorithm and Modification Using Gain Ratio. IOP Conference Series: Materials Science and Engineering, 420, 012133. https://doi.org/10.1088/1757-899X/420/1/012133">[Crossref]

  37. Rosas-Chavoya, M., Gallardo-Salazar, J. L., López-Serrano, P. M., Alcántara-Concepción, P. C., & León-Miranda, A. K. (2022). QGIS a Constantly Growing Free and Open-Source Geospatial Software Contributing to Scientific Development. Cuadernos de Investigación Geográfica, 48(1), 197–213. https://doi.org/10.18172/cig.5143">[Crossref]

  38. Seyam, M. M. H., Haque, M. R., & Rahman, M. M. (2023). Identifying the Land Use Land Cover (LULC) Changes Using Remote Sensing and GIS Approach: A Case Study at Bhaluka in Mymensingh, Bangladesh. Case Studies in Chemical and Environmental Engineering, 7, 100293. https://doi.org/10.1016/j.cscee.2022.100293">[Crossref]

  39. Shamrat, F. M. J. M., Ranjan, R., Hasib, K. M., Yadav, A., & Siddique, A. H. (2022). Performance Evaluation Among ID3, C4.5, and CART Decision Tree Algorithm (pp. 127–142). https://doi.org/10.1007/978-981-16-5640-8_11">[Crossref]

  40. Ślawska, J. (2024). Shaping the Rural Landscape: Institutions of Land Use Change in Non-Urbanized Areas in Poland. Sustainability, 16(24), 10902. https://doi.org/10.3390/su162410902">[Crossref]

  41. Su, H., Peng, Y., Xu, C., Feng, A., & Liu, T. (2021). Using Improved DeepLabv3+ Network Integrated with Normalized Difference Water Index to Extract Water Bodies in Sentinel-2A Urban Remote Sensing Images. Journal of Applied Remote Sensing, 15(01). https://doi.org/10.1117/1.JRS.15.018504">[Crossref]

  42. Syahrani, I. M. (2019). Comparation Analysis of Ensemble Technique With Boosting(Xgboost) and Bagging (Randomforest) For Classify Splice Junction DNA Sequence Category. Jurnal Penelitian Pos Dan Informatika, 9(1), 27–36. https://doi.org/10.17933/jppi.v9i1.249">[Crossref]

  43. Tharwat, A. (2021). Classification Assessment Methods. Applied Computing and Informatics, 17(1), 168–192. https://doi.org/10.1016/j.aci.2018.08.003">[Crossref]

  44. Trivedi, N. K., Gautam, V., Anand, A., Aljahdali, H. M., Villar, S. G., Anand, D., Goyal, N., & Kadry, S. (2021). Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network. Sensors, 21(23), 7987. https://doi.org/10.3390/s21237987">[Crossref]

  45. Valkenborg, D., Rousseau, A.-J., Geubbelmans, M., & Burzykowski, T. (2023). Support Vector Machines. American Journal of Orthodontics and Dentofacial Orthopedics, 164(5), 754–757. https://doi.org/10.1016/j.ajodo.2023.08.003">[Crossref]

  46. Vujovic, Ž. Ð. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 12(6). https://doi.org/10.14569/IJACSA.2021.0120670">[Crossref]

  47. Widaningrum, D. L. (2022). The Effect of Supporting Facilities Growth Around the Urban Campus on Land-Use Change. Proceedings of the International Conference on Engineering and Information Technology for Sustainable Industry, 1–6. https://doi.org/10.1145/3557738.3557948">[Crossref]

  48. Yao, X., & Li, G. (2018). Big Spatial Vector Data Management: A Review. Big Earth Data, 2(1), 108–129. https://doi.org/10.1080/20964471.2018.1432115">[Crossref]

  49. Zhai, H., Lv, C., Liu, W., Yang, C., Fan, D., Wang, Z., & Guan, Q. (2021). Understanding Spatio-Temporal Patterns of Land Use/Land Cover Change under Urbanization in Wuhan, China, 2000–2019. Remote Sensing, 13(16), 3331. https://doi.org/10.3390/rs13163331">[Crossref]

  50. Zheng, Y., Tang, L., & Wang, H. (2021). An Improved Approach for Monitoring Urban Built-Up Areas by Combining NPP-VIIRS Nighttime Light, NDVI, NDWI, and NDBI. Journal of Cleaner Production, 328, 129488. https://doi.org/10.1016/j.jclepro.2021.129488">[Crossref]

  51. Zhou, J., Qiu, Y., Chen, J., & Chen, X. (2021). A Geometric Misregistration Resistant Data Fusion Approach for Adding Red-Edge (RE) and Short-Wave Infrared (SWIR) Bands to High Spatial Resolution Imagery. Science of Remote Sensing, 4, 100033. https://doi.org/10.1016/j.srs.2021.100033">[Crossref]

  52. Zhu, L., Song, R., Sun, S., Li, Y., & Hu, K. (2022). Land Use/Land Cover Change and Its Impact on Ecosystem Carbon Storage in Coastal Areas of China from 1980 to 2050. Ecological Indicators, 142, 109178. https://doi.org/10.1016/j.ecolind.2022.109178">[Crossref]


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