Applications of remotely sensed data in flood prediction and monitoring: report of the CEOS Disaster Management Support Group flood team
Bibliographic record
Abstract
The potential of high and low resolution polar and geostationary orbital Earth Resource Satellites have been shown to be an excellent tool for providing hydrological information. Operational geostationary meteorological satellites have the capability to provide precipitation estimates and soil wetness indices at the global scale, while polar orbital satellites can provide the quantification of catchment physical characteristics, such as topography and land use, and catchment variables such as soil moisture and snow cover. There have been many demonstrations of the operational use of these satellites for detailed monitoring and mapping of floods and post-flood damage assessment. This paper addresses the use of Earth Observation satellites for flood managers, flash flood analysis and prediction, and the user community. A remote sensing management cycle is presented that involves: (1) prevention where history, corporate memory, and climatology are important; (2) mitigation that insulates people or infrastructure from hazards; (3) pre-flood which is the preparation and forecast stage where remote sensing is essential; (4) response (during the flood) where "actions to be taken is of key importance and weather NOWCASTS (0-3 hour prediction of precipitation) using remote sensing is extremely useful; and (5) recovery (post flood) which is the post-mortem stage where damage assessment, procedures, and numerical weather prediction and hydrological models are validated. Gaps in our remote sensing capabilities, future improvements and requirements, and the requirement for demonstration projects to illustrate and educate the end-user community on the capabilities of satellite remotely sensed data to provide information during all of the phases of the disaster cycle are discussed.
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".