Remote sensing identification of industrial solid waste and open pits in mining areas based on the multiscale sample set optimization strategy

doi:10.6046/zrzyyg.2023385

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Abstract

A timely and accurate understanding of the spatial extents and distributions of industrial solid waste and open pits in mining areas is significant for the precise control of solid waste contamination and the ecosystem conservation. Remote sensing technology is an effective monitoring method. However, single-scale sample sets fail to fully represent the features of industrial solid waste yards and open pits with different shapes and sizes. Constructing multiscale sample sets may be effective in solving the problem of incomplete feature representation for different industrial solid waste yards and open pits, thereby enhancing the identification accuracy and generalization capability of models. By fully considering the differences in the shape and size of different industrial solid waste yards and open pits, this study proposed a remote sensing identification method for industrial solid waste and open pits based on the multiscale sample set optimization strategy. In the proposed method, a multiscale sample set was prepared based on the preprocessed data of the GF-1B, GF-1C, and GF6 satellite remote sensing images. Subsequently, a U-Net deep learning network model was constructed to identify industrial solid waste and open pits. Finally, the identification accuracy was compared with that of the single-scale sample set model. The results show that the U-Net deep learning network model based on the multiscale sample set achieved identification accuracy of 81.23 %, recall of 66.88 %, F1-score of 73.36 %, and average intersection over union of 73.55 %, suggesting improvements by 6.02, 1.02, 3.12, and 9.86 percentage points, respectively, compared to the single-scale sample set model. Overall, this study provides a reliable approach for precisely monitoring industrial solid waste and open pits.

Keywords multiscale deep learning industrial solid waste remote sensing identification

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Issue Date: 01 July 2025

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	Haijing ZOU
	Bin ZOU
	Yulong WANG
	Bo ZHANG
	Lunwen ZOU

Cite this article:

Haijing ZOU,Bin ZOU,Yulong WANG, et al. Remote sensing identification of industrial solid waste and open pits in mining areas based on the multiscale sample set optimization strategy[J]. Remote Sensing for Natural Resources, 2025, 37(3): 1-8.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023385 OR https://www.gtzyyg.com/EN/Y2025/V37/I3/1

Tab.1 Description of remote sensing image data

Tab.2 Remote sensing interpretation symbols for industrial solid waste and open-pit mining areas

Fig.1 Distribution of industrial solid waste samples and open-pit mining areas

Fig.2 Structure diagram of U-Net neural network

Tab.3 Model parameter

Tab.4 Comparison of recognition accuracy on different scale sample set models(%)

Fig.3-1 Loss functions of different scale sample set model

Fig.3-2 Precision of different scale sample set model

Tab.5 The comparison of recognition results of different scale sample set models

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