Dem Fusion of Elevation REST API Data in Support of Rapid Flood Modelling
Bibliographic record
Abstract
Digital elevation models (DEM) are an integral part of flood modelling. High resolution DEM data are not always available or affordable for communities, thus other elevation data sources are explored. While the accuracy of some of these sources has been rigorously tested (e.g., SRTM, ASTER), others, such as Natural Resources Canada’s Canadian Digital Elevation Model (CDEM) and Google and Bings’ Elevation REST APIs, have not yet been properly evaluated. Details pertaining to acquisition source and accuracy are often unreported for APIs. To include these data in geospatial applications and test and reduce uncertainty, data fusion is explored. Thus, this paper introduces a new method of elevation data fusion. The novel method incorporates clustering and inverse distance weighting (IDW) concepts in the computation of a new fusion elevation surface. The results of the individual DEMs and fusion DEMs are compared to high-resolution Light Detection and Ranging (LiDAR) surface and flood inundation maps for two study areas in New Brunswick. Comparison of individual surfaces to LiDAR find that the results meet their posted accuracy specifications, with the Bing data computing the smallest mean bias and the CDEM the smallest RMSE. Fusion of all three surfaces via the proposed method increases the correlation and minimizes both RMSE and mean bias when compared to LiDAR, independent of the terrain, thus producing a more accurate DEM.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".