Construction of digital 3D highway model using stereo IKONOS satellite imagery
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Bibliographic record
Abstract
Abstract This study aims to assess the accuracy of using stereo high resolution satellite imagery for extracting the highway profiles and plans and constructing accurate 3D highway visualization model. Two stereo-pair IKONOS satellite images for Hong Kong and Toronto are geo-referenced by using a number of ground control points acquired by global positioning system measurements. A polynomial-based generic pushbroom model and rational function model are used to perform the sensor orientation, respectively. The highway alignments are extracted semi-automatically using stereoscopic measurements, and a 3D digital model along the highway is constructed. It is found that the highway alignments retrieved from the stereo IKONOS images result in less than 1-m root mean squared error in most of the cases in the horizontal and vertical directions. Near half-pixel accuracy can be achieved by using pansharpening stereo satellite imagery and under the condition that clear road surface markings can be identified along the highway. Keywords: stereo IKONOS satellite imagerysatellite sensor modellingdigital 3D modelhighway alignmentsaccuracy assessment Acknowledgements This study was financially supported by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada. The authors express their sincere gratitude to several people for their help in this study: Prof. Ahmed El-Rabbany, Mr. Mohamed Elsobeiey, Mr. Amit Joshi, Mr. Jonathan Kwon, Mr. Hilbert Wong and Mr. Timothy Chin from Ryerson University for their help in the GPS field measurements and data processing in Toronto; Mr. Kevin Kwan for his assistance in the GPS field measurements in Hong Kong; Mr. Donald Choi for acquiring the stereo aerial images in Hong Kong; and Prof. Zhilin Li from the Hong Kong Polytechnic University provided the IKONOS satellite images from the CERG project ‘Optimum Compression of One-Meter Satellite Images for mapping purposes'. The authors also would like to thank Mr. Paul Vincent from the Ministry of Transportation of Ontario for providing the topographic map data of Toronto.
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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 | 0.000 | 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 it