Open-pit mining area extraction based on deep learning and object-oriented image analysis in the Weining Beishan area

doi:10.13474/j.cnki.11-2246.2024.0209

Abstract

Abstract: Weining Beishan is a key area for the restoration and management of mining ecological environment in Ningxia. Quickly and accurately extracting information of the open-pit mining area and monitoring the progress of ecological restoration have become important tasks in the mining management. This article proposes a method for extracting information on open-pit mining areas from domestically produced high-resolution remote sensing satellite images in the Weinin Beishan area, which combines deep learning and object-oriented analysis. The method first uses the U-Net model, which supports small-sample learning, to perform initial recognition of the open-pit mining area, and then combines object-oriented analysis with spatial analysis methods to extract the mining area boundary. The experimental results show that the accuracy of identifying the spatial location of open-pit mining areas is 0.71, and the average spatial range extraction accuracy is 0.78. Based on this, the paper identifies and analyzes the ecological restoration status of open-pit mines in the Weining Beishan area from 2019 to 2021. Among the identified 125 open-pit mining areas, 43.2% have been restored, including 44 filled and leveled pits, 6 areas that have been redeveloped, and 4 areas that have been artificially revegetated. The method proposed in this article can accurately extract vector boundaries of open-pit mining areas without the need for feature engineering and can provide technical reference for mining remote sensing monitoring in Ningxia.

Key words: Weining Beishan area, open-pit mine, deep learning, object-oriented, remote sensing monitoring

CLC Number:

P237

LIU Li, LI Shiyao, WANG Run, LIU Shaoyu, SONG Yongfei, NIU Ruiqing. Open-pit mining area extraction based on deep learning and object-oriented image analysis in the Weining Beishan area[J]. Bulletin of Surveying and Mapping, 2024, 0(2): 51-57.

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