High-Precision Measurement of Height Differences from Shadows in Non-Stereo Imagery: New Methodology and Accuracy Assessment
Bibliographic record
Abstract
The shadow-height method has been extensively used to extract the heights of buildings from the shadows they cast in non-stereo (single view) aerial and satellite imagery. However, the use of this method in Earth sciences has been limited, partially due to the relatively low accuracy reported, the fuzziness of shadow edges, the complexities of the scanning sensors, and a lack of software tools. In this paper, we present an enhanced shadow-height methodology offering significant accuracy improvement. These improvements are mainly the result of using a physical approach to model the illumination gradient through the edge of shadows and by leveraging meteorological data to precisely estimate atmospheric refraction. We validated 91 shadow-derived height estimations from images obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) at three sites with latitudes between 33 and 78°S: The Andes Mountains, Sentinel Range, and Abbot ice shelf. Reference measurements were obtained from Global Navigation Satellite System (GNSS) surveys and the Ice, Cloud, and land Elevation Satellite (ICESat). The observed errors fell below 6% for small height differences (∼20 m) and below 2% for larger height differences (≳300 m). Our validation data cover solar elevations ranging from 3.7 to 42.2°, and we observed smaller absolute errors at lower solar elevations. This novel information can be valuable for studying surface elevation changes in present and old imagery and extending glacier volume variation time-series.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".