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Related Concept Videos

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

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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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Ambient noise-based weakly supervised manhole localization methods over deployed fiber networks.

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    This study introduces a novel manhole localization method using distributed fiber optic sensing and machine learning. Ambient data is leveraged for underground cable mapping, improving efficiency and reducing fieldwork.

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    Area of Science:

    • Geophysics
    • Machine Learning
    • Telecommunications Engineering

    Background:

    • Underground cable mapping is crucial for operational efficiency in utility networks.
    • Current methods often require extensive fieldwork and detailed site information.
    • Ambient environmental data offers a potential alternative for non-intrusive mapping.

    Purpose of the Study:

    • To develop and validate a manhole localization method using distributed fiber optic sensing.
    • To explore the use of ambient environmental data for underground cable mapping.
    • To enhance operational efficiency and reduce fieldwork in cable infrastructure management.

    Main Methods:

    • Utilized distributed fiber optic sensing for data acquisition.
    • Employed weakly supervised machine learning techniques, specifically an attention-based deep multiple instance classification model.
    • Implemented a selective data sampling scheme to handle weakly informative ambient data.

    Main Results:

    • Successfully localized manholes using ambient environmental data.
    • Demonstrated the feasibility of using fiber sensing for underground infrastructure mapping.
    • Validated the approach on field data from existing fiber networks.

    Conclusions:

    • Ambient environmental data, when processed with advanced machine learning, can be effectively used for manhole localization.
    • The proposed method offers a promising, less labor-intensive alternative for underground cable mapping.
    • This technique has the potential to significantly enhance operational efficiency in managing buried cable networks.