Canada's Emergency Geomatics Service Near Real-Time Flood Mapping from Multi-Source Data
Bibliographic record
Abstract
The Canadian Emergency Geomatics Service (EGS) creates and disseminates flood maps in near real time during major flood events. In 2017, the EGS developed and deployed a fully automated method to map open water and flooded vegetation using available optical and radar imagery. This method employs machine learning trained using historical inundation maps to classify open water followed by region growing to map flooded vegetation, and requires only a few sensor-specific parameters for different input satellite data. Recent collection of High-Resolution Digital Elevation Model (HRDEM) lidar data over important floodplains, in Canada, in combination with in-situ flood perimeter observations from an EGS-developed Citizen Geographic Information (CGI) mobile application or other sources, has facilitated the development of better urban flood mapping where traditional satellite-based methods fail. This presentation will describe existing operational EGS flood mapping methods with examples from previous activations, as well as new developments in urban flood mapping that will become operational depending on the availability ofrequired input data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".