Effectiveness of aerial and ISERV-ISS RGB photos for real-time urban floodwater mapping: case of Calgary 2013 flood
Bibliographic record
Abstract
High-resolution red-green-blue (RGB) images from remote sensors, such as those carried on aircrafts, UAVs, satellites, and the International Space Station (ISS), are cost-effective data sources for real-time emergency response applications. We describe an assessment undertaken on spectral behaviors to evaluate the effectiveness of two high-resolution RGB image datasets for mapping and monitoring of floodwater extent in dense urban areas. The assessment was as part of a case study of the Calgary 2013 flood event. The input imagery included very high-resolution aerial photos and imagery acquired with the SERVIR Environmental Research and Visualization System (ISERV) carried on the ISS. The results demonstrate the complementary nature of these two RGB image sets in providing effective urban floodwater mapping for real-time response. The aerial photos with higher spatial resolution and less atmospheric effect can provide the details about the floodwater distribution; the images from ISERV-ISS can provide the temporal variation of floodwater distribution.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".