An efficient approach for image-DSM co-registration for urban building extraction
Bibliographic record
Abstract
Very high resolution (VHR) satellite images are the ideal geo-data for mapping urban areas. The co-registration of such images with elevation data is crucial for accurate 3D-supported building extraction and mapping applications. However, VHR satellite images are usually acquired off-nadir. Over urban areas, off-nadir images suffer from severe building lean caused by the images' perspective view. On the other hand, the elevations of digital surface models (DSM) are usually of orthographic projection. Such a difference makes pixel-by-pixel co-registration very challenging unless the DSM data are modified to be of Line-of-Sight projection (LoS-DSM). Therefore, this paper introduces a novel image-DSM co-registration method for building extraction. Based on generating disparity maps, the method constructs a perfectly co-registered LoS-DSM which is more efficiently than traditional algorithms. The root-mean-square-error of the developed LoS-DSM elevations was found to be less than 2 pixels relative to the traditional photogrammetric approach. Additionally, these elevations are of pixel-level co-registration accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".