The generation of digital elevation models using open source images
Bibliographic record
Abstract
Digital Elevation Models are a representation of Earth’s surface and are used in many areas of research. There are a number of freely available DEMs with near-global coverage, which have elevation accuracies ranging between 10 to 25 m. This project attempts to generate DEMs of comparable accuracy using open source images from satellite sensors and web mapping services. Images from Landsat 8, ASTER, and Sentinel-2 satellites, and from Microsoft’s Bing Maps were used to generate DEMs for a 6.633 km2 area in Oshawa, Canada. It was found that it is key that when combining images from different spaceborne sensors, the spatial resolution should be within 10 m of one another. Additionally, the radiometry of the images, in terms of intensity and contrast, must be similar. The highest accuracies of DEMs had RMSE values of 20.047 m and 20.579 m, when combining images from Sentinel-2 with ASTER and Landsat 8, respectively.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".