Automated urban land use and land cover classification for mesoscale atmospheric modeling over Canadian cities
Bibliographic record
Abstract
An automated geospatial database processing approach has been developed to characterize the urban areas of major Canadian cities for use in mesoscale atmospheric modeling. Mesoscale atmospheric numer-ical models, including urban canopy models such as the Town Energy Balance (TEB) model, require sur-face characteristics to represent surface processes that occur in cities. The methodology developed in this study uses the following pan-Canadian databases: the National Topographic Data Base (NTDB) vector data for land use and land cover (LULC) characterization, the Shuttle Radar Topography Mission (SRTM-DEM) and the Canadian Digital Elevation Data (CDED1) digital elevation models (DEM) for building height assessment, and census data for characteristics of residential districts. These databases are jointly processed to automatically generate a high-resolution urban LULC classification for Canadian cities. The main benefits of this approach are (a) Canada-wide applicability with available continuous databases, and (b) complete automation, with the exception of optional post-processing. 1.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".