Assessing the Compatibility of Machine Learning Tools for Mapping Impervious Surfaces of Urban Areas: A Geospatial Analysis of the Dhaka Metropolitan Area (DMPA)

Md. Sazedur Rahman

Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Bangladesh.

Mahisha Islam Kuasha

Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Bangladesh.

Amir Fosial

Department of Civil Engineering, International Islamic University Chittagong, Bangladesh.

Md. Abdul Kadir 

Department of Mathematics, University of Houston, Texas, USA.

Shoeb Ahmad Tanim

Department of Civil Engineering, International Islamic University Chittagong, Bangladesh.

Corresponding author:1716007@wre.buet.ac.bd (Md. Sazedur Rahman)

Keywords: Impervious surfaces, Urban growth, GIS-RS, Dhaka, Landsat.  

PDF: Full Article

DOI: https://doi.org/10.66268/jrse.2026.05.917341 

Published: 31 March 2026

Abstract

Rapid and unplanned urbanization in the Dhaka city area has significantly accelerated the expansion of impervious surfaces, altering the hydrological balance, intensifying surface runoff, and increasing environmental vulnerability. Therefore, the accurate mapping of impervious surfaces is crucial for evaluating urban growth patterns and designing sustainable, climate-resilient infrastructure. Hence, this study assesses the current impervious-surface landscape and evaluates the performance compatibility of manual digitization and machine-learning-based supervised classification techniques for mapping impervious surfaces across the Dhaka Metropolitan Area (DMPA) using geospatial techniques. Here, the manual digitization estimated the total impervious coverage at 138.08 km² (46.23%), while the machine learning (ML)-based supervised classification produced 155.68 km² (52.12%), revealing a 5.89% overestimation, primarily due to mixed-pixel effects at a 30 m spatial resolution. The classification achieved an overall accuracy of 88.4% and a Kappa coefficient (κ) of 0.84, indicating strong agreement with reference data. The results depict that the most urbanized areas, such as Lalbagh, Sutrapur, Kotwali, and Dhanmondi, exhibited the highest imperviousness. In contrast, planned or peripheral areas, such as Gulshan, Uttara, and Pallabi, retained larger pervious zones due to structured development and better land-use planning. The impervious coverage of Dhaka city far exceeds the ecological sustainability threshold (10%), signifying a critical hydrological imbalance and increased heat risks. The findings affirm that the MLC method is a reliable, scalable tool for urban surface analysis when complemented with high-resolution validation data. This study highlights the urgent need for prioritizing the restoration of green–blue infrastructure, the expansion of pervious surfaces, and the integration of a remote-sensing-based monitoring system to mitigate the escalating environmental pressures of rapid urban growth. 

References

Abbasi, M., Popy, S., Yumin, T. (2022). Quantifying spatiotemporal changes of the urban impervious surface of Dhaka District using remote sensing technology. Indonesian Journal of Geography, 54(3). https://doi.org/10.22146/ijg.72506

Ahmad, M. N., Shao, Z., Xiao, X., Fu, P., Javed, A., Ara, I. (2024). A novel ensemble learning approach to extract urban impervious surface based on machine learning algorithms using SAR and optical data. International Journal of Applied Earth Observation and Geoinformation, 132, 104013. https://doi.org/10.1016/j.jag.2024.104013

BBS (Bangladesh Bureau of Statistics). (2022). Statistical yearbook of Bangladesh 2022. Dhaka, Bangladesh.

Bashar, T., Uddin, M. Z. (2025). Effects of land use change on surface runoff and infiltration: The case of Dhaka City. Urban Science, 9(12), 497. https://doi.org/10.3390/urbansci9120497

Chen, L., Zhou, B., Man, W., Liu, M. (2020). Landsat-based monitoring of the heat effects of urbanization directions and types in Hangzhou City from 2000 to 2020. Remote Sensing, 13(21), 4268. https://doi.org/10.3390/rs13214268

Faridatul, M. I., Wu, B., Zhu, X. (2019). Assessing long-term urban surface water changes using multi-year satellite images: A tale of two cities, Dhaka and Hong Kong. Journal of Environmental Management, 243, 287–298. https://doi.org/10.1016/j.jenvman.2019.05.019

Faisal, A., Kafy, A. A., Al Rakib, A., Akter, K. S., Jahir, D. M. A., Sikdar, M. S., Ashrafi, T. J., Mallik, S., Rahman, M. M. (2021). Assessing and predicting land use/land cover, land surface temperature, and urban thermal field variance index using Landsat imagery for the Dhaka Metropolitan area. Environmental Challenges, 4, 100192. https://doi.org/10.1016/j.envc.2021.100192

Hu, Y., Zhan, H., He, Q., Zhan, W. (2024). Assessment of atmospheric correction algorithms for Landsat-8/9 operational land imager over inland and coastal waters. Remote Sensing, 17(17), 3055. https://doi.org/10.3390/rs17173055

Jombo, S., Adelabu, S. (2023). Evaluating Landsat-8, Landsat-9 and Sentinel-2 imageries in land use and land cover (LULC) classification in a heterogeneous urban area. GeoJournal, 88(Suppl 1), 377–399. https://doi.org/10.1007/s10708-023-10982-8

Kocaman, B., Ağaçcıoğlu, H. (2024). Assessment of the impact of land use/land cover changes on carbon emissions using remote sensing and deep learning: A case study of the Kağıthane Basin. Sustainability, 17(23), 10690. https://doi.org/10.3390/su172310690

Kulsum, U., Moniruzzaman, M. (2022). Exploring the relationship of climate change and land-use dynamics with satellite-derived surface indices and temperature in greater Dhaka, Bangladesh. Journal of Earth System Science, 131, 117. https://doi.org/10.1007/s12040-022-01841-0

Li, C., Chen, B., Wang, X., Wu, Z., Lan, G., Azmi Tohiran, K., Amit, B., Lai, H., Wang, G., Yun, T., Kou, W. (2024). Integrating multi-temporal Landsat and Sentinel data for enhanced oil palm plantation mapping and age estimation in Malaysia. Remote Sensing, 17(16), 2908. https://doi.org/10.3390/rs17162908

Li, Y., Yang, X., Wu, B., Zhao, J.,  Deng, X. (2023). Impervious surface mapping based on remote sensing and an optimized coupled model: The Dianchi basin as an example. Land, 12(6), 1210. https://doi.org/10.3390/land12061210

Mondal, I., Bandyopadhyay, J., Hossain, S. A., et al. (2025). Evaluating the effects of rapid urbanization on the encroachment of the East Kolkata Wetland ecosystem: A remote sensing and hybrid machine learning approach. Environment, Development and Sustainability, 27, 14781–14813. https://doi.org/10.1007/s10668-024-05832-7

Moniruzzaman, M., Thakur, P. K., Kumar, P., Ashraful Alam, M., Garg, V., Rousta, I., Olafsson, H. (2020). Decadal urban land use/land cover changes and its impact on surface runoff potential for the Dhaka City and surroundings using remote sensing. Remote Sensing, 13(1), 83. https://doi.org/10.3390/rs13010083

Rentachintala, L. R. N. P., Reddy, M. G. M., Mohapatra, P. K. (2022). Urban stormwater management for sustainable and resilient measures and practices: A review. Water Science and Technology, 85(4), 1120–1140. https://doi.org/10.2166/wst.2022.017

Shao, Z., Cheng, T., Fu, H., Li, D., Huang, X. (2022). Emerging issues in mapping urban impervious surfaces using high-resolution remote sensing images. Remote Sensing, 15(10), 2562. https://doi.org/10.3390/rs15102562

Sohn, W., Kim, J., Li, M., Brown, R. D., Jaber, F. H. (2020). How does increasing impervious surfaces affect urban flooding in response to climate variability? Ecological Indicators, 118, 106774. https://doi.org/10.1016/j.ecolind.2020.106774

Sonet, M. S., Hasan, M. Y., Kafy, A., Shobnom, N. (2025). Spatiotemporal analysis of urban expansion, land use dynamics, and thermal characteristics in a rapidly growing megacity using remote sensing and machine learning techniques. Theoretical and Applied Climatology, 156, 79. https://doi.org/10.1007/s00704-024-05264-3

Soufiane, I. M., Djaouad, R. D., Farah, B., Djamel, S. (2025). Spatiotemporal impact of urbanization on urban heat island using Landsat imagery in Oran, Algeria: 1984–2024. Urban Science, 9(4), 95. https://doi.org/10.3390/urbansci9040095

Stern, A. J., Daughtry, C. S., Hunt, E. R., Gao, F. (2022). Comparison of five spectral indices and six imagery classification techniques for assessment of crop residue cover using four years of Landsat imagery. Remote Sensing, 15(18), 4596. https://doi.org/10.3390/rs15184596

Su, S., Tian, J., Dong, X., Tian, Q., Wang, N., & Xi, Y. (2022). An Impervious Surface Spectral Index on Multispectral Imagery Using Visible and Near-Infrared Bands. Remote Sensing, 14(14), 3391. https://doi.org/10.3390/rs14143391

Sun, C., Chen, H., Fan, F. (2019). Improving accuracy of impervious surface extraction based on a threshold hierarchical method (THM). Applied Sciences, 10(23), 8409. https://doi.org/10.3390/app10238409

Van Nguyen, M., La, O. T., Nguyen, H. T. T., et al. (2025). Landsat 8 OLI atmospheric correction neural network for inland waters in tropical regions. International Journal of Environmental Science and Technology, 22, 6769–6788. https://doi.org/10.1007/s13762-024-06080-y

Wu, Y., Pan, J. (2023). Detecting changes in impervious surfaces using multi-sensor satellite imagery and machine learning methodology in a metropolitan area. Remote Sensing, 15(22), 5387. https://doi.org/10.3390/rs15225387

Xu, L., Sun, G., Zhang, A., Han, Z., Li, Z., Zhao, Y. (2025). Automatic mapping of high-resolution impervious surfaces driven by hierarchical adaptive features. Science of Remote Sensing, 12, 100300. https://doi.org/10.1016/j.srs.2025.100300

Yan, L., Roy, D. (2025). Using Landsat 8 and 9 operational land imager (OLI) data to characterize geometric distortion and improve geometric correction of Landsat multispectral scanner (MSS) imagery. Remote Sensing of Environment, 321, 114679. https://doi.org/10.1016/j.rse.2025.114679

Zhang, J., Du, J., Fang, S., Sheng, Z., Zhang, Y., Sun, B., Mao, J., Li, L. (2022). Dynamic changes, spatiotemporal differences, and ecological effects of impervious surfaces in the Yellow River Basin, 1986–2020. Remote Sensing, 15(1), 268. https://doi.org/10.3390/rs15010268

How to cite: 

Rahman, S. M., Kuasha, M. I., Fosial  A., Kadir  M. A., Tanim S. A. (2026). Assessing the Compatibility of Machine Learning Tools for Mapping Impervious Surfaces of Urban Areas: A Geospatial Analysis of the Dhaka Metropolitan Area (DMPA). Journal of Remote Sensing and Environment, 5, 20250503. https://doi.org/10.66268/jrse.2026.05.917341

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