An assessment of the utility of LiDAR data in extracting base-year floorspace and a comparison with the census-based approach
Bibliographic record
Abstract
A literature review indicates that most integrated land-use transport models (ILUTMs) estimate base-year floorspace data according to limited population and employment data provided by the census and, in general, the accuracy is unknown. This paper assesses the utility of airborne Light Detection And Ranging (LiDAR) technology as a valuable tool for extracting base-year floorspace using the following three datasets: a geographic vector building footprint layer, a LiDAR dataset, and field survey data for the south side of the City of Fredericton, Canada. It is found through a statistical comparison with the results from the field survey that LiDAR data can be used to extract buildings and estimate floorspace with a good degree of accuracy. Further, two base-year floorspace estimation methods, one based on the LiDAR data and the other on census data, are compared. In general, our results show that the traditional census-based approach may not be reliable for estimating base-year floorspace. Using the extracted floorspace from the LiDAR data as the basis, for residential floorspace estimation the average absolute percentage errors (APE) of the census-based approach is 16% and the 95th percentile APE is 34%. On the other hand, for employment floorspace estimation, the accuracy of the census-based approach is even lower, with average errors of 50% or higher and the 95th percentile APEs as high as 163% up to 400% for several land-use categories. All the above statistics indicate that the traditional census-based approach is unreliable and inaccurate for modelers and planners to prepare their base-year floorspace, and therefore suggest a better way be explored. Study results clearly show the utility of LiDAR data and imply that it can be used as a powerful add-on for ILUTMs in general.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".