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Applied Sciences
Volume 16
Issue 10
10.3390/app16104820
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

CS-DeepLabV3+: A Fine-Grained Semantic Segmentation Method for Mining Land Use in the Kunlun Mountain Region Using High-Resolution Remote Sensing Imagery

by
Yue Qi
1,
Zizhao Zhang
1,2,*,
Yang Hu
1,
Peizhi Liu
1,
Min Gao
1 and
Gaoyang Zhai
1
1
College of Geology and Mining Engineering, Xinjiang University, Urumqi 830046, China
2
Key Laboratory of Green and Efficient Mining and Ecological Restoration in High-Altitude Arid Areas of Xinjiang, Urumqi 830047, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(10), 4820; https://doi.org/10.3390/app16104820 (registering DOI)
Submission received: 30 March 2026 / Revised: 9 May 2026 / Accepted: 11 May 2026 / Published: 12 May 2026
Versions Notes

Abstract

Mining areas in high-altitude cold and arid mountains exhibit heterogeneous land-cover types, large spatial extent, and fragmented boundaries, which makes large-area monitoring difficult with manual interpretation. This study proposes CS-DeepLabV3+, an enhanced semantic segmentation framework built upon DeepLabV3+ for 1-m optical imagery in the Kunlun Mountains. A contextual modeling block is inserted between the encoder output and the atrous spatial pyramid pooling module to strengthen long-range dependency modeling under complex backgrounds. In the decoder, a channel attention block is applied to fused features to suppress redundant responses and improve separability among confusing categories. Experiments on a self-built dataset (Kunlun-Set) demonstrate improved boundary delineation and region consistency for typical mining-related classes (e.g., tailings ponds, stockpiles, and industrial yards). CS-DeepLabV3+ achieved an 81.83% mean intersection-over-union on the test set, outperforming the DeepLabV3+ baseline by 3.52 percentage points. Ablation studies verify that contextual modeling and channel recalibration provide complementary gains.
Keywords: high-resolution remote sensing; semantic segmentation; mining feature recognition; DeepLabV3+; contextual transformer (COT); channel attention (SENetV2); Kunlun Mountains; ecological restoration monitoring high-resolution remote sensing; semantic segmentation; mining feature recognition; DeepLabV3+; contextual transformer (COT); channel attention (SENetV2); Kunlun Mountains; ecological restoration monitoring

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MDPI and ACS Style

Qi, Y.; Zhang, Z.; Hu, Y.; Liu, P.; Gao, M.; Zhai, G. CS-DeepLabV3+: A Fine-Grained Semantic Segmentation Method for Mining Land Use in the Kunlun Mountain Region Using High-Resolution Remote Sensing Imagery. Appl. Sci. 2026, 16, 4820. https://doi.org/10.3390/app16104820

AMA Style

Qi Y, Zhang Z, Hu Y, Liu P, Gao M, Zhai G. CS-DeepLabV3+: A Fine-Grained Semantic Segmentation Method for Mining Land Use in the Kunlun Mountain Region Using High-Resolution Remote Sensing Imagery. Applied Sciences. 2026; 16(10):4820. https://doi.org/10.3390/app16104820

Chicago/Turabian Style

Qi, Yue, Zizhao Zhang, Yang Hu, Peizhi Liu, Min Gao, and Gaoyang Zhai. 2026. "CS-DeepLabV3+: A Fine-Grained Semantic Segmentation Method for Mining Land Use in the Kunlun Mountain Region Using High-Resolution Remote Sensing Imagery" Applied Sciences 16, no. 10: 4820. https://doi.org/10.3390/app16104820

APA Style

Qi, Y., Zhang, Z., Hu, Y., Liu, P., Gao, M., & Zhai, G. (2026). CS-DeepLabV3+: A Fine-Grained Semantic Segmentation Method for Mining Land Use in the Kunlun Mountain Region Using High-Resolution Remote Sensing Imagery. Applied Sciences, 16(10), 4820. https://doi.org/10.3390/app16104820

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