Multi-Scale Graph Wavelet Convolution for Hyperspectral-LiDAR Urban Scene Classification
Bibliographic record
Abstract
The Houston 2013 benchmark conducted in Houston describes a methodology that constructs a sparse pixel graph exclusively over labeled pixels. This approach employs learnable multi-scale spectral filtering using Chebyshev-approximated graph wavelets and incorporates a lightweight Multi-Layer Perceptron (MLP) head for classification purposes. The training procedure integrates feature MixUp at labeled nodes, a composite loss function combining focal loss and label smoothing, exponential moving average (EMA) weight tracking, an AdamW optimizer with warm-up and cosine scheduling, and mild graph augmentation techniques such as random edge drop and addition with re-normalization. Implementation executed precisely in accordance with the original methodology, with five independent runs producing an overall accuracy (OA) of 90.99% ± 0.41%, average accuracy (AA) of 92.11% ± 0.35%, and a Kappa coefficient (κ) of 0.9022 ± 0.0044. These results demonstrate not only high accuracy but also minimal variability, providing reassurance of the robustness of the methodology.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".