Impacts of light detection and ranging (LiDAR) data organization and unit of analysis on land cover classification
Bibliographic record
Abstract
Airborne light detection and ranging (LiDAR) data have been used to generate land cover models for almost two decades. In this paper, three common processing decisions are assessed for their impact on the accuracy and configuration of the resultant land cover models. Using data acquired from a single-wavelength, discrete return system, this study compares six land cover models that investigate (i) the organization of data into tiles or flightstrips, (ii) the unit of analysis as either the individual LiDAR point or as a pixel in a rasterized model of the LiDAR data, and (iii) the use of either pixel- or object-based image analysis. Although the overall accuracies of the land cover models generated in this study are comparable, models disagree on up to 17% of the total study area. Class-specific metrics of recall and precision differ markedly between models, and the configuration of land covers are also affected. Models that employ pixel-based image analysis techniques tend to generate models with smaller, more dispersed patches of land cover. Data organization and choice of unit of analysis also influence the configuration of land cover, although effects differ depending on the land cover class. Comprehensive analyses of accuracy and precision are crucial to developing land cover models. This study demonstrates that it is also important to understand the potential influence of classification methodologies on the configuration of landscape features, especially when interpreting land cover models from an ecological or landscape genetic perspective.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".