Extraction of gobi desert gravel layer based on U-ConvHDNet

doi:10.13474/j.cnki.11-2246.2025.0412

Abstract

Abstract: The gravel layer is an essential component of the gobi desert ecosystem. Conducting large-scale remote sensing monitoring of the gravel layer is of great significance for protecting the gobi desert ecology. In response to the loose structure and strong heterogeneity of the gravel layer, this paper proposes an automatic information mapping method for the gravel layer based on the U-ConvHDNet semantic segmentation model. This method utilizes Sentinel-2 imagery from the entire Hami region captured in August 2023 to extract information on gobi gravel layer. The results indicate that the F1 score of the U-ConvHDNet model is 0.918, which is superior to that of the other seven semantic segmentation models. Ablation experiments demonstrate that the combined use of the improved backbone network, upsampling and downsampling modules effectively enhances the accuracy. The dual receptive field sliding window strategy optimizes the instability near stitching lines, enabling the extraction of the total area of the gobi gravel layer in Hami at 1.026×10⁵ km², with an information extraction precision of F1 score 0.921. This study provides technical support for monitoring of gobi gravel layer and the management of gobi ecosystems.

Key words: remote sensing imagery, semantic segmentation, deep learning, gravel layer

CLC Number:

MA Yubo, ZHANG Aiguo, WANG Haoyu, LIU Shuaiqi, JIN Jingyu, SHEN Zhanfeng, LI Junli. Extraction of gobi desert gravel layer based on U-ConvHDNet[J]. Bulletin of Surveying and Mapping, 2025, 0(4): 68-74.

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