Building Extraction From Fused LiDAR and Hyperspectral Data using Random Forest Algorithm
Bibliographic record
Abstract
In this study, the fusion of Light Detection and Ranging (LiDAR) and hyperspectral data was used to propose a method for building detection. The number of hyperspectral bands was first reduced from 144 to 8 layers using the Linear Discriminant Analysis (LDA) algorithm to remove highly redundant bands and reduce computational costs. Then, these layers were integrated with 4 layers of heights and intensities obtained from the LiDAR data. The fused layers (12 layers) were applied to a Random Forest (RF) algorithm to extract the boundaries of buildings. Finally, two morphological operators were applied to remove the holes on the buildings’ roofs and repair their boundaries. A comparison was also performed between the results obtained by the proposed method and the reference study in this field [Debes et al. 2014]. The proposed method demonstrated a better accuracy for building detection in a much shorter time compared to the refer ence method. The values of 97% and 96% were obtained for producer and user accuracies, respectively. Overall, the method presented in this study proved to have a high potential for building extraction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".