Multiple regression model for estimating vertical characteristics of built-up areas at 100 m resolution from open and global Digital Elevation Models
Bibliographic record
Abstract
Detailed and spatially consistent information on the three-dimensional characteristics of built-up areas is a key element in monitoring global urban development. We construct a regression-based method for the estimation of the vertical components of built-up areas in 100 m resolution, capturing the average height and the standard deviation of height of buildings in relation to the open spaces around them. We used open and globally available Digital Elevation Models (DEMs) fused with ancillary remote-sensing products as the model inputs, and tested a combination of radar-based and optical-based DEMs as the source of building height information. We develop and test our model on selected cities: Albuquerque, Beirut, London, Philadelphia, San Francisco and Toronto. Results of our model show realistic spatial patterns and low error for tested areas (RMSE 0.95 and R20.62 for Average Gross Building Height component). We demonstrated feasibility of estimating vertical components of built-up areas in 100 m resolution using a method developed under the basic assumptions of: availability of open data, low cost of the analyses conducted, and transparency and reproducibility of the results obtained.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".