Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture

İlyas Aslan; Nizar Polat

Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture

Abstract

Photogrammetric methods have advanced significantly, enabling progress in cartography, construction, agriculture, and natural disaster monitoring. The integration of Structure from Motion (SfM) and orthophoto mapping has facilitated the generation of high-resolution, error-corrected images for various geospatial analyses. However, traditional deep learning-based Convolutional Neural Networks (CNNs) for orthophoto classification face challenges such as high computational costs, limited multiscale feature extraction, and suboptimal accuracy in complex landscapes. To address these limitations, this study introduces Multiscale Inception Depthwise Network (MIDNet), a novel CNN-based architecture designed for efficient and precise classification of UAV-derived high-resolution orthophotos. MIDNet leverages inception modules for multiscale feature extraction and depthwise separable convolutions to enhance computational efficiency without sacrificing performance. Experimental validation conducted on the generated reference dataset demonstrates that MIDNet outperforms the compared deep learning models, achieving an overall accuracy of 96.97%, an average accuracy of 95.96% and a kappa coefficient of 96.29%, surpassing DenseNet121 (OA: 96.32%, AA: 95.47%, Kappa: 95.50%) and InceptionV3 (OA: 96.60%, AA: 94.05%, Kappa: 95.85%), while maintaining the smallest model size (4.05 MB) and fastest testing time (8 seconds). These results underscore MIDNet’s superior classification accuracy, lightweight design, and suitability for resource-constrained environments, making it a compelling advancement in orthophoto classification techniques.

Keywords

Ethical Statement

This article does not contain any studies with human participants or animals performed by any of the authors

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Details

Primary Language

English

Subjects

Photogrammetry and Remote Sensing

Journal Section

Research Article

Authors

İlyas Aslan ^*
0000-0003-4388-6633
Türkiye

Nizar Polat
0000-0002-6061-7796
Türkiye

Publication Date

March 30, 2026

Submission Date

February 8, 2026

Acceptance Date

March 17, 2026

Published in Issue

Year 2026 Volume: 8 Number: 1

IZ

https://izlik.org/JA49RX58YD

Cite

RIS / Bibtex

APA

Aslan, İ., & Polat, N. (2026). Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture. Turkish Journal of Applied Geoinformation Sciences, 8(1), 15-27. https://izlik.org/JA49RX58YD

AMA

1.Aslan İ, Polat N. Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture. Turk. J. Appl. Geoinf. Sci. 2026;8(1):15-27. https://izlik.org/JA49RX58YD

Chicago

Aslan, İlyas, and Nizar Polat. 2026. “Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture”. Turkish Journal of Applied Geoinformation Sciences 8 (1): 15-27. https://izlik.org/JA49RX58YD.

EndNote

Aslan İ, Polat N (March 1, 2026) Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture. Turkish Journal of Applied Geoinformation Sciences 8 1 15–27.

IEEE

[1]İ. Aslan and N. Polat, “Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture”, Turk. J. Appl. Geoinf. Sci., vol. 8, no. 1, pp. 15–27, Mar. 2026, [Online]. Available: https://izlik.org/JA49RX58YD

ISNAD

Aslan, İlyas - Polat, Nizar. “Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture”. Turkish Journal of Applied Geoinformation Sciences 8/1 (March 1, 2026): 15-27. https://izlik.org/JA49RX58YD.

JAMA

1.Aslan İ, Polat N. Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture. Turk. J. Appl. Geoinf. Sci. 2026;8:15–27.

MLA

Aslan, İlyas, and Nizar Polat. “Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture”. Turkish Journal of Applied Geoinformation Sciences, vol. 8, no. 1, Mar. 2026, pp. 15-27, https://izlik.org/JA49RX58YD.

Vancouver

1.İlyas Aslan, Nizar Polat. Deep Learning-Based Classification of UAV Orthophotos Using MIDNet Architecture. Turk. J. Appl. Geoinf. Sci. [Internet]. 2026 Mar. 1;8(1):15-27. Available from: https://izlik.org/JA49RX58YD