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

Design Example: Alignment of a Road Line Using GIS

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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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Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
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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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When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
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Sight Distance in a Vertical Curve01:29

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Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
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A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Detecting Road Obstacles by Erasing Them.

Krzysztof Lis, Sina Honari, Pascal Fua

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 29, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method for detecting road obstacles by inpainting image patches to remove them. The system identifies obstacles by detecting differences between original and inpainted images, improving road safety.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Autonomous Driving Systems

    Background:

    • Road obstacle detection is crucial for autonomous vehicles.
    • Pre-recording all possible obstacles for training is infeasible.
    • Existing methods struggle with novel or uncatalogued obstacles.

    Purpose of the Study:

    • To develop a robust obstacle detection method for vehicles.
    • To address the challenge of detecting previously unencountered road obstacles.
    • To improve the safety and reliability of autonomous driving systems.

    Main Methods:

    • Image patches are selected from road scenes.
    • Obstacles within patches are removed using inpainting with surrounding road texture.
    • A neural network is trained to identify discrepancies between original and inpainted patches.
    • Detected discrepancies signify the presence of an obstacle.

    Main Results:

    • The inpainting technique effectively removes obstacles from selected image patches.
    • The discrepancy detection network accurately identifies the location of removed obstacles.
    • The method shows promise in detecting a wide range of previously unrecorded obstacles.

    Conclusions:

    • This novel approach offers a viable solution for detecting uncatalogued road obstacles.
    • The discrepancy-based detection method enhances the robustness of obstacle detection systems.
    • Further development could significantly improve autonomous vehicle safety.