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Full Scale Fusion Building Extraction Network with Coordinate Attention and Convolution Enhancement
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Rui-li HE1, 2, Wei-peng LE3, You YU1, 2, Liang HUANG3, *
Science Technology and Engineering | 2025, 25(18) : 7485 - 7492
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Science Technology and Engineering | 2025, 25(18): 7485-7492
Papers·Astronomy and Geosciences
Full Scale Fusion Building Extraction Network with Coordinate Attention and Convolution Enhancement
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Rui-li HE1, 2, Wei-peng LE3, You YU1, 2, Liang HUANG3, *
Affiliations
  • 1 Hunan Geological Hazards Investigation and Monitoring Institute, Changsha 410004, China
  • 2 Hunan Survey and Design Institute Co., Ltd., Changsha 410004, China
  • 3 School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Published: 2025-06-28 doi: 10.12404/j.issn.1671-1815.2404716
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Buildings are important carriers of human production activities. Accurate and fast extraction of building areas can play an important role in the field of natural resource management. Although significant progress has been made in building extraction from remote sensing images based on CNN(convolutional neural network), the constructed network model still needs to be optimized in feature extraction and feature fusion. Therefore, a coordinate attention and CCFNet(convolutional enhanced full-scale fusion building extraction network) was proposed. The constructed model consists of a residual encoder enhanced by coordinate attention and convolution and a full-scale fusion decoder. Coordinate attention was used in the encoder to build inter-channel dependencies and capture global information. The asymmetric convolution was used to enhance the edge feature extraction of ground objects, and it is more robust to rotation, flip distortion and uneven aspect ratio of ground objects. The full-scale fusion method used in the decoder helps to reconstruct the buildings. The experimental results on the dataset of typical Chinese city buildings show that compared with other building extraction networks, The CCFNet model constructed in this paper achieves the best experimental Accuracy of 93.84%, 84.08%, 72.53% and 82.59% in the four segmentation evaluation indicators of accuracy, F1, IOU and MIOU, respectively. Experimental results show that the model can effectively extract building regions.

coordinate attention  /  full scale fusion  /  building extraction  /  asymmetric convolution
Rui-li HE, Wei-peng LE, You YU, Liang HUANG. Full Scale Fusion Building Extraction Network with Coordinate Attention and Convolution Enhancement[J]. Science Technology and Engineering, 2025 , 25 (18) : 7485 -7492 . DOI: 10.12404/j.issn.1671-1815.2404716
Year 2025 volume 25 Issue 18
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doi: 10.12404/j.issn.1671-1815.2404716
  • Receive Date:2024-06-24
  • Online Date:2025-12-17
  • Published:2025-06-28
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  • Received:2024-06-24
  • Revised:2025-03-19
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Affiliations
    1 Hunan Geological Hazards Investigation and Monitoring Institute, Changsha 410004, China
    2 Hunan Survey and Design Institute Co., Ltd., Changsha 410004, China
    3 School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
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表12种不同金属材料的力学参数

Family
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Number of
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种数
Number of
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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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