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An open−pit mine segmentation method based on SAM and prompt learning
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Mingze SUN1, Jun WANG1, Wanqiu ZHANG2, *, Kun LIU3, Gang LIN4, 5, *
Science & Technology Review | 2026, 44(6) : 76 - 82
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Science & Technology Review | 2026, 44(6): 76-82
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An open−pit mine segmentation method based on SAM and prompt learning
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Mingze SUN1, Jun WANG1, Wanqiu ZHANG2, *, Kun LIU3, Gang LIN4, 5, *
Affiliations
  • 1School of Civil Engineering, Shandong Jianzhu University, Jinan 250101, China
  • 2College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
  • 3Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
  • 4Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 5College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Published: 2026-03-28 doi: 10.3981/j.issn.1000-7857.2025.12.00121
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Accurately acquiring the spatial distribution of open−pit mines is vital for "green mine" development and dynamic geological monitoring. To overcome the inherent challenges of dataset scarcity, drastic intra− and inter−class differences, and complex topological structures in this field, we propose a segmentation framework integrating hybrid semantic prompting and topological awareness. We constructed a specific dataset named Mine Semantic Segmentation (MSS). MSS contains 7,622 finely annotated images of open−pit mines. Based on MSS, we propose an instance segmentation method called Mine Segment Anything Model (Mine−SAM). Mine−SAM employs a dual−encoder structure. It also utilizes multi−scale feature aggregation techniques. The model couples global context from foundation models with local fine−grained features from expert models. Mine−SAM achieved an Average Precision box (PA,box) of 64.4%. The Average Precision mask (PA,mask) score reached 65.2%. In addition, we developed a semantic segmentation method named SemMSeg. This method combines graph convolutional networks (GCN) with pixel−level contrastive learning. The GCN captures spatial dependencies among mining elements. It also enforces structural constraints within the model. SemMSeg achieved an Intersection over Union (IoU) of 73.38%. The precision of the method reached 85.03%. These techniques provide a technical path for automatic mine monitoring. The findings contribute to the intelligent interpretation of remote sensing imagery.

open−pit mine segmentation  /  instance segmentation  /  prompt learning  /  graph convolution network
Mingze SUN, Jun WANG, Wanqiu ZHANG, Kun LIU, Gang LIN. An open−pit mine segmentation method based on SAM and prompt learning[J]. Science & Technology Review, 2026 , 44 (6) : 76 -82 . DOI: 10.3981/j.issn.1000-7857.2025.12.00121
Year 2026 volume 44 Issue 6
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Article Info
doi: 10.3981/j.issn.1000-7857.2025.12.00121
  • Receive Date:2025-12-10
  • Online Date:2026-04-16
  • Published:2026-03-28
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  • Received:2025-12-10
  • Revised:2026-03-01
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Affiliations
    1School of Civil Engineering, Shandong Jianzhu University, Jinan 250101, China
    2College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
    3Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
    4Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    5College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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