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相关概念视频

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Field Application of Global Positioning System01:28

Field Application of Global Positioning System

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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从分类中定位:用于远程传感图像的自导弱监控对象定位.

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    科学领域:

    • 计算机科学 计算机科学
    • 遥感 遥感 遥感 遥感
    • 人工智能的人工智能

    背景情况:

    • 在远程传感图像 (RSI) 中对象定位和检测对于各种应用至关重要.
    • 完全监督的方法需要广泛,昂贵的实例级注释.
    • 弱监督对象定位 (WSOL) 使用图像级标签提供了一个具有成本效益的替代方案.

    研究的目的:

    • 为RSIs中的对象定位提出一种新的自我指导的弱监督策略 (SD-WSS).
    • 加强空间特征提取并解决WSOL中的歧视性区域问题.
    • 为了减少背景干扰并提高复杂RSIs的定位精度.

    主要方法:

    • 利用和增强分类模型的空间特征提取能力.
    • 使用GradCAM++提取空间位置信息以指导学习过程.
    • 设计了一种新的自导损失函数,以优化模型焦点和减轻背景噪声.

    主要成果:

    • 拟议的SD-WSS方法在与主流WSOL方法相比显示出更高的性能.
    • 在新创建的WSOL基准 (C45V2和PN2) 上进行了广泛的实验.
    • 该方法有效地定位了感兴趣的对象,尽管RSI的复杂背景.

    结论:

    • SD-WSS策略在遥感图像中显著提升了弱监督的对象定位.
    • 该方法提供了一种切实可行的解决方案,可以减少注释工作,同时保持高局部化准确度.
    • 开发的基准和提出的方法有助于推动远程传感领域的WSOL研究.