Machine Learning-Based Land Cover Mapping for Environmental and Urban Planning Applications: A Cloud Computing Framework Using Multi-Source Geospatial Data

Document Type : Original Article

Authors

1 Department of Remote Sensing & GIS, SR. C., Islamic Azad University, Tehran, Iran

2 Department of Geoinformation and Geomatics Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

3 Department of Civil Engineering, SR. C., Islamic Azad University, Tehran, Iran

4 Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran

Abstract

Accurate land cover information is essential for environmental management, urban planning, and sustainable development. This study presents a geospatial data fusion framework for land cover mapping using Google Earth Engine (GEE). The framework integrates Landsat 9 imagery, five spectral indices (NDVI, NDWI, NDBI, BSI, and SAVI), and ALOS Digital Surface Model (DSM) data. The methodology was applied in Hamedan Province, Iran, to classify four land cover classes: water bodies, vegetation, urban areas, and bare lands. Five supervised machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), Gradient Tree Boosting (GTB), and Minimum Distance (MD)—were evaluated. Classification performance was assessed using overall accuracy, producer accuracy, user accuracy, and the Kappa coefficient. Results showed that integrating spectral and topographic features improved class separability and classification accuracy. RF achieved the best performance with an overall accuracy of 98% and a Kappa coefficient of 0.97, followed by GTB. In contrast, MD produced lower accuracy and was more affected by spectral confusion. The findings demonstrate the effectiveness of cloud-based machine learning and multi-source geospatial data fusion for accurate land cover mapping and support applications in environmental monitoring, resource management, and spatial planning.

Keywords

References

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Interdisciplinary Journal of Civil Engineering
Volume 2, Issue 1 - Serial Number 5
January 2026
Pages 468-491

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