Optical remote sensing for urban flood applications: Canadian case studies
Bibliographic record
Abstract
Floods are the most common disaster in Canada. As results of rapid urbanization and climate changes, both frequency and risks of floods have been increased in Canadian urbanized areas, where the disasters have usually costlier impacts than in rural areas. Imagery data and technologies of optical remote sensing are helpful and can be applied for urban flood response and pre-disaster preparation. Especially high and very high optical remote sensing can be used for precise mapping of the floodwater distribution in dense urban areas and providing key information for disaster response management. In addition, the geospatial information about urban land surface and urban growth derived from optical remote sensing imagery can be the key inputs for urban flood risk analyses. In recent years, several case studies for different urban flood types, including fluvial (Calgary 2013, Ottawa-Gatineau 2017) and pluvial (the Greater Toronto Area) floods in Canada, have been carried out at Canada Centre for Mapping and Earth Observation, Natural Resources Canada. Methodologies/framework for urban floodwater mapping have been developed based on high resolution optical data, as well as the impacts of urban growth on the urban flash flood risks have been investigated using model simulations with remote sensing derived maps as inputs. This presentation demonstrates results from three Canadian urban flood case studies and introduces remote-sensing-based methodologies for different types of urban floods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".