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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Mine ecological restoration is a critical process for promoting the sustainable development of resource-dependent regions, yet existing monitoring methods remain limited in accuracy and adaptability. To address challenges such as small-object recognition, insufficient multi-scale feature fusion, and blurred boundaries in UAV-based remote sensing imagery, this paper proposes an enhanced semantic segmentation model based on Segformer. Specifically, a multi-scale feature-enhanced feature pyramid network (MSFE-FPN) is introduced between the encoder and decoder to strengthen cross-level feature interaction. Additionally, a selective feature aggregation pyramid pooling module (SFA-PPM) is integrated into the deepest feature layer to improve global semantic perception, while an efficient local attention (ELA) module is embedded into lateral connections to enhance sensitivity to edge structures and small-scale targets. A high-resolution UAV image dataset, named the HUNAN Mine UAV Dataset (HNMUD), is constructed to evaluate model performance, and further validation is conducted on the public Aeroscapes dataset. Experimental results demonstrated that the proposed method exhibited strong performance in terms of segmentation accuracy and generalization ability, effectively supporting the image analysis needs of mine restoration scenes.

Details

Title
An Improved Segformer for Semantic Segmentation of UAV-Based Mine Restoration Scenes
Author
Wang, Feng 1   VIAFID ORCID Logo  ; Zhang Lizhuo 1   VIAFID ORCID Logo  ; Jiang, Tao 2 ; Li Zhuqi 3   VIAFID ORCID Logo  ; Wu Wangyu 4   VIAFID ORCID Logo  ; Kuang Yingchun 1   VIAFID ORCID Logo 

 College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; [email protected] (F.W.); [email protected] (L.Z.) 
 Technology Innovation Center for Ecological Conservation and Restoration in Dongting Lake Basin, Ministry of Natural Resources, Changsha 410004, China; [email protected], Hunan Center of Natural Resources Affairs, Changsha 410004, China 
 School of Computer and Control Engineering, Northeast Forestry University, Harbin 150006, China; [email protected] 
 School of Computer Science, University of Liverpool, Liverpool L69 3DR, UK; [email protected] 
First page
3827
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3223942180
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.