Photogrammetry Statistics


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Photogrammetry Statistics 2023: Facts about Photogrammetry outlines the context of what’s happening in the tech world.

LLCBuddy editorial team did hours of research, collected all important statistics on Photogrammetry, and shared those on this page. Our editorial team proofread these to make the data as accurate as possible. We believe you don’t need to check any other resources on the web for the same. You should get everything here only 🙂

Are you planning to form an LLC? Maybe for educational purposes, business research, or personal curiosity, whatever the reason is – it’s always a good idea to gather more information about tech topics like this.

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On this page, you’ll learn about the following:

Top Photogrammetry Statistics 2023

☰ Use “CTRL+F” to quickly find statistics. There are total 12 Photogrammetry Statistics on this page 🙂

Photogrammetry “Latest” Statistics

  • Photos taken in a sequence should overlap by at least 60% – if there is meager overlap, the model will not develop correctly or at all.[1]
  • Four points representing 17.4% of the H-dataset data in the DAM slope were deleted from the total of 23 points utilized for validation.[2]
  • From 2020 to 2030, the employment of cartographers and photogrammetrists is anticipated to increase by 5%, which is a slower rate than the national average for all professions.[2]
  • The kinetic energy of the fragments is comprised of 12% –24% of the absorbed energy, and the fraction climbs as the strain rate increases.[2]
  • The slope was greater than 40% and the checkpoints placed in the model’s border occurred at low precision due to the interpolation methods during the photogrammetric reconstruction.[2]
  • Camera positions are assessed by matching related features in pairs of images involving 60% to 80% of scene overlap and less than 30º between the images.[2]
  • From 2021 to 2031, the employment of cartographers and photogrammetrists is anticipated to increase by 3%, albeit more slowly than the average for all professions.[3]
  • SfM has significant promise for forest mapping in underdeveloped nations since more than half of the world’s forest-covered regions have a canopy cover of 50%.[4]
  • Given a confidence level of 5% and 22 degrees of freedom, the findings suggest that there is a trend in the E-N discrepancies, suggesting systemic mistakes that may be investigated further using circular statistic approaches.[5]
  • Due to the interpolation techniques used during the photogrammetric reconstruction, the slope was larger than 40%, and the checkpoints situated in the model’s boundary produced poor accuracy.[5]
  • With at least 90% confidence and 22 degrees of freedom, the ortho mosaic fit in Class D on a scale of 1:2,000 and in Class B on a scale of 1:5000.[5]
  • Accuracy-checking standards presume that at least 90% of the data goes down within a range of the mean plus two times the standard deviation.[5]

Also Read

How Useful is Photogrammetry

One of the key advantages of photogrammetry is its ability to create detailed, accurate 3D models of objects or landscapes using nothing but photographs. This can be particularly valuable in industries such as surveying and land management, where the ability to quickly and accurately map large areas is crucial. For archaeologists and historians, photogrammetry offers a non-invasive way to document and study ancient artifacts and sites with unprecedented accuracy.

Furthermore, photogrammetry can also be a cost-effective solution for tasks that previously required expensive equipment or time-consuming manual labor. For example, in construction and engineering, photogrammetry can be used to monitor the progress of a project, assess the condition of structures, and even detect potential hazards. In agriculture, photogrammetry can help farmers optimize their crop production by analyzing vegetation health, soil composition, and water distribution.

However, despite its many advantages, photogrammetry does have its limitations. One of the main challenges of using photogrammetry is the need for high-quality images to ensure accurate results. Factors such as lighting conditions, camera settings, and image resolution can all impact the quality of a photogrammetric model. This can be particularly challenging in outdoor settings where environmental conditions are unpredictable and uncontrollable.

Another limitation of photogrammetry is its reliance on advanced software and specialized skills. While there are many user-friendly photogrammetry software programs available on the market, achieving reliable and accurate results still requires a great deal of expertise and experience. This can be a barrier for some industries and organizations that may not have access to trained professionals or the resources to invest in training.

Furthermore, photogrammetry is not a foolproof solution for every application. There are certain situations where other technologies, such as LiDAR or drone surveys, may be more suitable for the task at hand. Understanding the strengths and limitations of different surveying methods is key to selecting the right tool for the job.

In conclusion, while photogrammetry is undoubtedly a powerful and versatile tool, its usefulness ultimately depends on the specific needs and constraints of each project. By leveraging its strengths and mitigating its limitations, industries and organizations can harness the full potential of photogrammetry to streamline workflows, improve efficiencies, and make informed decisions based on accurate data.

Reference

  1. wiley – https://onlinelibrary.wiley.com/doi/10.1111/phor.12299
  2. webinarcare – https://webinarcare.com/best-photogrammetry-software/photogrammetry-statistics/
  3. bls – https://www.bls.gov/ooh/architecture-and-engineering/cartographers-and-photogrammetrists.htm
  4. mdpi – https://www.mdpi.com/1999-4907/8/3/68
  5. tandfonline – https://www.tandfonline.com/doi/full/10.1080/22797254.2020.1717998

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