Photogrammetric and lidar data integration using the centroid of a rectangular roof as a control point
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The integration of photogrammetric images and lidar data is becoming a powerful procedure that can be applied in the optimisation of photogrammetric mapping techniques. The complementary nature of lidar and photogrammetric data optimises the performance of many procedures used to extract 3D spatial information from data. For example, photogrammetric imagery enables the accurate extraction of building borders and lidar provides accurate 3D points that give information on the physical surfaces of buildings. These properties demonstrate the usefulness of combining the two types of data to achieve a more robust and complete reconstruction of 3D objects. Photogrammetric procedures require the exterior orientation parameters (EOPs) of the images to extract mapping information. Despite the availability of GPS/INS systems, which greatly assist in direct georeferencing of the imagery, the majority of commercially available photogrammetric systems require control information in order to carry out photogrammetric mapping. Due to improvements in the accuracy of lidar systems in recent years, lidar data is considered a viable source of photogrammetric control. Point features are the principal source of control for photogrammetric triangulation, although linear features and planar patches have also been used. This paper presents a method of georeferencing photogrammetric images using lidar data. The method uses the centroids of rectangular building roofs as control points in the photogrammetric procedure. The centroid of a rectangular building roof derived using lidar data is equivalent to a single control point with 3D coordinates, and can therefore be used in traditional photogrammetric systems. Two photogrammetric experiments were carried out to verify the feasibility of the methodology. The results obtained from these experiments confirm the feasibility of applying the proposed methodology to the georeferencing of photogrammetric images using lidar data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it