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 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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".