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相关概念视频

Design Example: Alignment of a Road Line Using GIS01:17

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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相关实验视频

Updated: Jul 11, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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使用阿基米德调过程与量子扩展卷积神经网络提取道路.

Mohd Jawed Khan1, Pankaj Pratap Singh1, Biswajeet Pradhan2,3

  • 1Department of Computer Science & Engineering, Central Institute of Technology, Kokrajhar 783370, Assam, India.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
概括

本研究介绍了一种先进的深度学习模型,用于从遥感图像中自动提取道路网络. 这种新的方法显著提高了道路细分的准确性,超过了现有的方法.

关键词:
阿基米德的优化算法人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.扩张的卷曲 扩张的卷曲量子计算是一种量子计算.远程传感是一种遥感技术.道路开采工程 道路开采工程

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 从遥感 (RS) 图像中自动提取道路网络至关重要,但由于道路特性多样化,具有挑战性.
  • 传统方法与不同的道路特征作斗争,需要先进的技术来进行高分辨率的细分.

研究的目的:

  • 提出和评估阿基米德调过程量子扩展卷积神经网络用于道路提取 (ATP-QDCNNRE) 技术.
  • 通过使用深度学习和优化算法从RS数据中提取道路的精度和效率.

主要方法:

  • ATP-QDCNNRE方法使用量子扩展卷积神经网络 (QDCNN) 模型,结合量子计算概念和扩展卷积.
  • 使用阿基米德优化算法 (AOA) 进行超参数调整,以优化QDCNN模型用于道路开采.
  • 该模型捕获了本地和全球的上下文信息,同时保持了空间分辨率,用于精细的道路特征提取.

主要成果:

  • 在马萨诸塞州的道路数据集上,ATP-QDCNNRE方法取得了高性能.
  • 关键指标包括75.28%的交叉超过联盟 (IoU),95.19%的平均交叉超过联盟 (MIoU),90.85%的F1得分,87.54%的精度和94.41%的回忆.

结论:

  • ATP-QDCNNRE方法证明了从遥感图像中提取道路的卓越效率和准确性.
  • 深度学习,量子计算概念和高级优化的整合显著提高了细分能力.