Accuracy Assessment of Building Extraction Using LIDAR Data for Urban Planning/Transportation Applications
Bibliographic record
Abstract
Urban and transportation planning require land-use inventory (2D and 3D city models) to support the visualization and analysis of land-use patterns in the context of existing and future situations. These patterns impact travel behaviour, resulting in traffic volume and travel mode alteration. In particular, buildings as the containers of socio-economic activities are among the most important objects as they are constantly subjected to construction and destruction. Providing precise building information such as floor space data is a vital input for integrated land-use transportation models (ILUTM). A more cost-effective building information collection method is required to replace traditional ground survey techniques or estimation methods practiced in transportation related studies. Airborne laser scanning (LiDAR) is an established technology which can collect the location and elevation of the reflecting surfaces of large areas. By utilizing a normal LiDAR analysis program, this paper attempts to compare the accuracy of building information (e.g. building footprints and height) extracted from LiDAR data with the ground truth information at several zonal levels, e.g., Census block or tract, from the south side of the City of Fredericton. The accuracy of building information extracted from LiDAR data is quantified, and its applicability for land use and transportation is verified. Study results show that LiDAR technology is a timely and cost-effective approach for extracting building/land use information, and it can be considered as a valuable tool for sustainable urban/transportation planning. For teh covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".