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Open AccessArticle
Enhancing a Building Change Detection Model in Remote Sensing Imagery for Encroachments and Construction on Government Lands in Egypt as a Case Study
by
Essam Mohamed AbdElhamied
Essam Mohamed AbdElhamied
,
Sherin Moustafa Youssef
Sherin Moustafa Youssef
Marwa Ali ElShenawy
Marwa Ali ElShenawy
Gouda Ismail Salama
Gouda Ismail Salama
1
Information and Documentation Center, Arab Academy for Science & Technology (AASTMT), Alexandria 1029, Egypt
2
Computer Engineering Department, Arab Academy for Science & Technology (AASTMT), Alexandria 1029, Egypt
3
Department of Computer Engineering, Military Technical College (MTC), Cairo 11771, Egypt
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(17), 9407; https://doi.org/10.3390/app15179407 (registering DOI)
Submission received: 9 June 2025
/
Revised: 19 July 2025
/
Accepted: 16 August 2025
/
Published: 27 August 2025
Abstract
Change detection (CD) in optical remote-sensing images is a critical task for applications such as urban planning, disaster monitoring, and environmental assessment. While UNet-based architecture has demonstrated strong performance in CD tasks, it often struggles with capturing deep hierarchical features due to the limitations of plain convolutional layers. Conversely, ResNet architectures excel at learning deep features through residual connections but may lack precise localization capabilities. To address these challenges, we propose ResUNet++, a novel hybrid architecture that combines the strengths of ResNet and UNet for accurate and robust change detection. ResUNet++ integrates residual blocks into the UNet framework to enhance feature representation and mitigate gradient vanishing problems. Additionally, we introduce a Multi-Scale Feature Fusion (MSFF) module to aggregate features at different scales, improving the detection of both large and small changes. Experimental results on multiple datasets (EGY-CD, S2Looking, and LEVIR-CD) demonstrate that ResUNet++ outperforms state-of-the-art methods, achieving higher precision, recall, and F1-scores while maintaining computational efficiency.
Share and Cite
MDPI and ACS Style
AbdElhamied, E.M.; Youssef, S.M.; ElShenawy, M.A.; Salama, G.I.
Enhancing a Building Change Detection Model in Remote Sensing Imagery for Encroachments and Construction on Government Lands in Egypt as a Case Study. Appl. Sci. 2025, 15, 9407.
https://doi.org/10.3390/app15179407
AMA Style
AbdElhamied EM, Youssef SM, ElShenawy MA, Salama GI.
Enhancing a Building Change Detection Model in Remote Sensing Imagery for Encroachments and Construction on Government Lands in Egypt as a Case Study. Applied Sciences. 2025; 15(17):9407.
https://doi.org/10.3390/app15179407
Chicago/Turabian Style
AbdElhamied, Essam Mohamed, Sherin Moustafa Youssef, Marwa Ali ElShenawy, and Gouda Ismail Salama.
2025. "Enhancing a Building Change Detection Model in Remote Sensing Imagery for Encroachments and Construction on Government Lands in Egypt as a Case Study" Applied Sciences 15, no. 17: 9407.
https://doi.org/10.3390/app15179407
APA Style
AbdElhamied, E. M., Youssef, S. M., ElShenawy, M. A., & Salama, G. I.
(2025). Enhancing a Building Change Detection Model in Remote Sensing Imagery for Encroachments and Construction on Government Lands in Egypt as a Case Study. Applied Sciences, 15(17), 9407.
https://doi.org/10.3390/app15179407
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