CapViT: Cross-context capsule vision transformers for land cover classification with airborne multispectral LiDAR data
Bibliographic record
Abstract
Equipped with multiple channels of laser scanners, multispectral light detection and ranging (MS-LiDAR) devices possess more advanced prospects in earth observation tasks compared with their single-band counterparts. It also opens up a potential-competitive solution to conducting land cover mapping with MS-LiDAR devices. In this paper, we develop a cross-context capsule vision transformer (CapViT) to serve for land cover classification with MS-LiDAR data. Specifically, the CapViT is structurized with three streams of capsule transformer encoders, which are stacked by capsule transformer (CapFormer) blocks, to exploit long-range global feature interactions at different context scales. These cross-context feature semantics are finally effectively fused to supervise accurate land cover type inferences. In addition, the CapFormer block parallels dual-path multi-head self-attention modules functioning to interpret both spatial token correlations and channel feature interdependencies, which favor significantly to the semantic promotion of feature encodings. Consequently, with the semantic-promoted feature encodings to boost the feature representation distinctiveness and quality, the land cover classification accuracy is effectively improved. The CapViT is elaborately testified on two MS-LiDAR datasets. Both quantitative assessments and comparative analyses demonstrate the competitive capability and advanced performance of the CapViT in tackling land cover classification issues.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".