An agent-based method for automatic building recognition from lidar data
Bibliographic record
Abstract
Light detection and ranging (lidar) as a modern and powerful remote sensing technology has proven to be a promising data source for modelling of various three-dimensional (3D) objects such as buildings and trees in urban areas. Nevertheless, because of the adjacency of buildings and other objects, especially trees, the results obtained from most traditional building-recognition algorithms are still dependent on several assumptions and simplifications. This paper presents a multi-agent methodology for automatic building recognition based on the decision-level fusion of textural and spatial information extracted from lidar range and intensity products. In the proposed methodology, two different groups of object-recognition agents are defined for building and tree detection in parallel. The algorithm has two different operational levels based on the types of contextual information. In the first level, both object-recognition agents decide on the types of objects in the study area based on textural information, and the candidates of the building and tree regions are generated. In the second operational level, building- and tree-recognition agents perform some operations at the macro level to modify the candidates of building and tree regions based on spatial information. Evaluation of the results confirms the significant capabilities of the proposed multi-agent algorithm to decrease the conflicts in the field of automatic building recognition in complex urban areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".