Assessment of GeoEye-1 stereo-pair-generated DEM in flood mapping of an ungauged basin
Bibliographic record
Abstract
A very high resolution (VHR) digital elevation model (DEM) is produced from a GeoEye-1 0.5-m-resolution satellite stereo pair and is used for floodplain management and mapping applications such as watershed delineation and river cross-section extraction. For this purpose, a 2 m × 2 m resolution terrain surface is produced from the stereo pair by using the Leica Photogrammetry Suite (LPS) enhanced Automatic Terrain Extraction (eATE) algorithm. DEM accuracy is assessed by comparison with measured individual ground control points (GCPs), stream cross-sections and other landscape features. Results show that the produced DEM is in good agreement with ground truth and superior to products of lower resolution, such as 90 m NASA Shuttle Radar Topography Mission (SRTM) and 1:5,000 topographical maps. One- and two-dimensional hydraulic models are used to simulate rainfall–runoff characteristics and flood wave kinematics of the flash flood event of 17 October 2006 that occurred in the ungauged basin of Almirida, using the 2 m VHR-DEM as an input. Results show that the hydraulic simulation based on the generated VHR-DEM, calibrated and validated via field data, produces an accurate extent and water level of the flooded area. Remote sensing stereo reconstruction is a promising alternative to traditional survey methods in flood mapping applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".