Multi-Scale Graph Wavelet Convolution for Hyperspectral-LiDAR Urban Scene Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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 it