The Use Of Photogrammetry Techniques To Evaluate The Construction Project Progress
Bibliographic record
Abstract
The modeling of 3D objects from image sequence is a challenging problem and has been an important research topic in the areas of photogrammetry and computer vision for many years. Photogrammetry is the science of calculating 3D object co–ordinates from image and provides a flexible and robust approach for measuring the static and dynamic characteristics for construction management. This paper discusses the experience in Construction Technology and Management Centre (CTMC), Universiti Teknologi Malaysia (UTM) in adapting photogrammetry methods for specific problems in the construction industry and outlines the principles of close–range photogrammetry in evaluating the progress of construction projects. There is a need to use the principles of close–range photogrammetry to evaluate the progress of construction project and to develop the actual progress bar chart. The fundamental task of photogrammetry is to rigorously establish the geometric relationship between the image and the object, as it existed at the time of imaging event. One such software application is PhotoModeler Pro version from the Canadian company Eos System has been suggested to extract the 3D features from 2D images. The approach described in this paper demonstrates the use of digital photogrammetry as a complementary method, which describes the 3D features extraction procedure in detail and highlights the qualitative control that can be achieved during the construction. The technique uses mainly off–the–shelf digitalcamer and software technologies that are affordable to most organisations and able to provide acceptable accuracy. Key words: Close–range photogrammetry, digital photographs, 3D–coordinates, software and digital monitoring system
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".