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Design Example: Measuring Distance Between Two Points with Obstructions01:10

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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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Electronic Distance Measuring Instruments01:30

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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over...
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Distance Corrections01:15

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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Common Leveling Mistakes and Errors01:17

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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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Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
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Indoor Object Measurement Through a Redundancy and Comparison Method.

Pedro Faria1, Tomás Simões2, Tiago Marques3

  • 1Infrastructure Department, Hainan University, Haikou 570228, China.

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|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a new computer vision framework for accurate indoor spatial measurement using standard smartphone cameras. It overcomes limitations of previous methods by leveraging geometry and architectural rules for precise wall and space dimension estimation.

Keywords:
SpatialLMautomatic optical inspectioncomputer visiondeep learninggeometry inferenceindoor spatial modelingindustrial quality inspectionmachine learningobject measurementreal estate image analysissensing technologiessmartphone-based sensing

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Area of Science:

  • Computer Vision
  • Geometric Deep Learning
  • Indoor Spatial Measurement

Background:

  • Conventional computer vision methods struggle with accurate object detection and measurement in indoor environments, especially in minimalistic spaces with limited visual references.
  • Existing models often fail to produce complete detections or accurate distance estimates for elements like walls that exceed the camera's field of view.

Purpose of the Study:

  • To introduce a novel geometry-driven, redundancy-based framework for enhancing the measurement accuracy of walls and spatial divisions in indoor settings.
  • To enable accurate spatial measurement using only standard smartphone cameras, without specialized hardware.

Main Methods:

  • Developed a framework leveraging proportional laws and architectural heuristics for measurement enhancement.
  • Trained the model on 204 labeled indoor images with data augmentation over 500 epochs.
  • Implemented a redundancy correction method to reduce distance deviation errors.

Main Results:

  • Achieved a mean average precision (mAP@50) of 0.995, precision of 0.995, and recall of 0.992, indicating model convergence and generalization.
  • The redundancy correction method reduced distance deviation errors to approximately 10%, with a mean absolute error below 2% in the use case.
  • The solution operates on 2D visual input, enabling on-device and offline use without specialized hardware.

Conclusions:

  • The proposed framework offers a scalable, low-cost alternative for accurate indoor spatial measurement using standard smartphone cameras.
  • Demonstrated the feasibility of camera-based geometry correction in real-world indoor environments.
  • Future work may integrate this method with multimodal models for full-room spatial reasoning in various applications.