Assessment of a SIFT-based DEM extraction approach using GEOEYE-1 satellite stereo-pairs in flood mapping of an ungauged basin
Bibliographic record
Abstract
A module for Digital Elevation Model (DEM) extraction from Very High Resolution (VHR) satellite stereo-pair imagery is developed and used for a flood plain mapping applications at an ungauged basin. A procedure for parallel processing of cascading image tiles is used for handling the large datasets requirements of VHR satellite imagery. The Scale-Invariant Feature Transform (SIFT) algorithm is used to detect potentially homogeneous features in the members of the stereo-pair. The resulting feature pairs are filtered using the RANdom SAmple Consensus (RANSAC) algorithm by using a variable distance threshold. Finally, homogeneous pairs are converted to point cloud ground coordinates for DEM generation. A 0.5 m × 0.5 m Geoeye-1 stereo-pair acquired over an area of 25 km2 in the island of Crete, Greece is used as input for the module. The resulting 1.5 m × 1.5 m DEM has superior detail over previously developed 2 m and 5 m DEMs that are used as reference, and yields a Root Mean Square Error (RMSE) of about 1 m compared to ground truth measurements. 1D and 2D hydraulic models are used to simulate the rainfall-runoff characteristics and the flood wave kinematics, of the flash flood event of October 17th, 2006 that occurred in Almirida basin, by using the 1.5 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, proving that Remote Sensing stereo reconstruction is a promising alternative to the 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".