Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images

Hüseyin Acemli; Nida Kumbasar

doi:10.54287/gujsa.1664093

Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images

Abstract

Recent advancements in deep learning have significantly contributed to the development of high spatial resolution (HSR) land cover mapping. However, the distinct geographic patterns between urban and rural areas have limited the generalizability of deep learning algorithms across these domains. To address this challenge, separate datasets for rural and urban environments have been proposed in the literature, aiming to achieve more reliable results in real-world applications. In this study, we utilize the publicly available LoveDA HSR dataset for model and parameter comparison. Experiments were conducted on two distinct scenarios: rural and urban areas. The combination of the Adam optimizer, Dice loss function, and UNet++ architecture exhibited the highest performance in both datasets. A weighted average of this combination, based on the number of test samples, was calculated for both groups, yielding a final performance score of 62.14% in terms of mean Intersection over Union (IoU).

Keywords

Supporting Institution

TÜBİTAK-BİLGEM

Thanks

This study was conducted in the TÜBİTAK-BİLGEM We would like to express our profound gratitude to TÜBİTAK-BİLGEM.

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other), Photogrammetry and Remote Sensing, Remote Sensing , Computational Modelling and Simulation in Earth Sciences

Journal Section

Research Article

Authors

Hüseyin Acemli
0009-0004-1804-4834
Türkiye

Nida Kumbasar ^*
0000-0001-5497-4618
Türkiye

Early Pub Date

June 11, 2025

Publication Date

June 30, 2025

Submission Date

March 25, 2025

Acceptance Date

May 26, 2025

Published in Issue

Year 2025 Volume: 12 Number: 2

DOI

https://doi.org/10.54287/gujsa.1664093

IZ

https://izlik.org/JA62RK86WN

Cite

RIS / Bibtex

APA

Acemli, H., & Kumbasar, N. (2025). Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images. Gazi University Journal of Science Part A: Engineering and Innovation, 12(2), 479-502. https://doi.org/10.54287/gujsa.1664093

AMA

1.Acemli H, Kumbasar N. Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images. GU J Sci, Part A. 2025;12(2):479-502. doi:10.54287/gujsa.1664093

Chicago

Acemli, Hüseyin, and Nida Kumbasar. 2025. “Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images”. Gazi University Journal of Science Part A: Engineering and Innovation 12 (2): 479-502. https://doi.org/10.54287/gujsa.1664093.

EndNote

Acemli H, Kumbasar N (June 1, 2025) Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images. Gazi University Journal of Science Part A: Engineering and Innovation 12 2 479–502.

IEEE

[1]H. Acemli and N. Kumbasar, “Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images”, GU J Sci, Part A, vol. 12, no. 2, pp. 479–502, June 2025, doi: 10.54287/gujsa.1664093.

ISNAD

Acemli, Hüseyin - Kumbasar, Nida. “Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images”. Gazi University Journal of Science Part A: Engineering and Innovation 12/2 (June 1, 2025): 479-502. https://doi.org/10.54287/gujsa.1664093.

JAMA

1.Acemli H, Kumbasar N. Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images. GU J Sci, Part A. 2025;12:479–502.

MLA

Acemli, Hüseyin, and Nida Kumbasar. “Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 12, no. 2, June 2025, pp. 479-02, doi:10.54287/gujsa.1664093.

Vancouver

1.Hüseyin Acemli, Nida Kumbasar. Comparison of Deep Learning Algorithms for Image Segmentation on Satellite Images. GU J Sci, Part A. 2025 Jun. 1;12(2):479-502. doi:10.54287/gujsa.1664093