A Building Detection Method Based on Semi-Suppressed Fuzzy C-Means and Restricted Region Growing Using Airborne LiDAR
Bibliographic record
Abstract
Building detection using airborne Light Detection And Ranging (LiDAR) data is the essential prerequisite of many applications, including three-dimensional city modeling. In the paper, we propose a coarse-to-fine building detection method that is based on semi-suppressed fuzzy C-means and restricted region growing. Based on a filtering step, the remaining points can be separated into two groups by semi-suppressed fuzzy C-means. The group contains points that are located on building roofs that form a building candidate set. Subsequently, a restricted region growing algorithm is implemented to search for more building points. The proposed region growing method perfectly ensures the rapid growth of building regions and slow growth of non-building regions, which enlarges the area differences between building and non-building regions. A two-stage strategy is then adopted to remove tiny point clusters with small areas. Finally, a minimum bounding rectangle (MBR) is used to supplement the building points and refine the results of building detection. Experimental results on five datasets, including three datasets that were provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) and two Chinese datasets, verify that most buildings and non-buildings can be well separated during our coarse building detection process. In addition, after refined processing, our proposed method can offer a high success rate for building detection, with over 89.5% completeness and a minimum 91% correctness. Hence, various applications can exploit our proposed method.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".