Use of lidar-derived NDTI and intensity for rule-based object-oriented extraction of building footprints
Bibliographic record
Abstract
Buildings play an essential role in urban intra-construction, planning, and climate. The precise knowledge of building footprints not only serves as a primary source for interpreting complex urban characteristics, but also provides regional planners with more realistic and multidimensional scenarios for urban management. The recently developed airborne light detection and ranging (lidar) technology provides a very promising alternative for building-footprint measurement. In this study, lidar intensity data, a normalized digital surface model (nDSM) of the first and last returns, and the normalized difference tree index (NDTI) derived from the two returns are used to extract building footprints using rule-based object-oriented classification. The study area is chosen in London, Ontario, based on the various types of buildings surrounded by trees. An integrated segmentation approach and a hierarchical rule-based classification strategy are proposed during the process. The results indicate that the proposed object-based classification is a very effective semi-automatic method for building-footprint extraction, with buildings and trees successfully separated. An overall accuracy of 94.0% and a commission error of 6.3% with a kappa value of 0.84 are achieved. Lidar-derived NDTI and intensity data are of great importance in object-based building extraction, and the kappa value of the proposed method is double that of the object-based method without NDTI or intensity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".