Bibliographic record
Abstract
This Thesis addresses the topic of the extraction of Digital Elevation Models (DEMs) from the in-track stereo images acquired by IKONOS satellite. Research on this topic is mainly motivated by the need of DEMs in trasportation and the potential use of very high resolution satellite stereo images to replace the traditional aerial photography to generate the DEMs that may be used for preliminary planning and location issues, limiting expensive and time consuming photogrammetry work to the final alignment corridor. In this thesis, two methods for DEM extraction from IKONOS stereo images using a modified Rational Function Model (RFM) and the 3D physical model developed at the Canada Centre for Remote Sensing (CCRS) are used and the accuracy of the DEMs generated using these two models are evaluated. The nominal accuracy of ground points determined with the vendor-supplied RPCs is evaluated and systematic biases are found. A significant improvement in the DEM accuracy is made by removing the biases in both the image and ground domain with the information of ground control. DEMs are automatically generated bya chain of processes using the PCI Geomatica OrthoEngine software package with the refined RFM and 3D physical model, respectively. The DEMs from these two methods are then compared in a desktop ERDAS Imagine environment and the accuracy of the DEMs is evaluated by comparing the extracted DEMs with the DEM from airphotos. The DEMs generated using different mathematical models have a very good consistence and more than 97% of the difference between the generated DEMs and the DEM from airphotos is between -2 m to 2m.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".