DSM generation and evaluation from QuickBird stereo imagery with 3D physical modelling
Bibliographic record
Abstract
A digital terrain model (DTM) extracted from QuickBird in-track stereo images using a three-dimensional (3D) multisensor physical model developed at the Canada Centre for Remote Sensing, Natural Resources Canada was evaluated. Firstly, the stereo photogrammetric bundle adjustment was set-up with about 10 accurate ground control points and 1-2 m errors in the three axes were obtained over 48 independent checkpoints. The DTM was then generated using an area-based multi-scale image matching method and 3D semi-automatic editing tools and then compared to lidar elevation data with 0.2-m accuracy. An elevation error with 68% confidence level (LE68) of 6.4 m was achieved over the full area. Since the DTM is in fact a digital surface model where the height, or a part, of land cover (trees, houses) is included, the accuracy depends on the land cover types. Using 3D visual classification of the stereo QuickBird images, different classes (deciduous, conifer, mixed and sparse forests, residential areas, bare soils and lakes) were generated to take into account the height of the surfaces (natural and human-made) in the accuracy evaluation. LE68 values of 3.4 m to 6.7 m were thus obtained depending on the land cover types with biases representative of the surface heights. On the other hand, LE68 values of 0.5 m and 1.3 m with no bias were obtained for lakes and bare soils respectively. These last results are more representative of the real stereo QuickBird potential for DTM and 5-m contour line generation, compliant with the highest topographic standard. Since the images were acquired in wintertime and the lidar data in summertime, better results could thus be expected when using stereo images acquired in summertime, mainly in deciduous forests to integrate the full canopy height into the DSM.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".