Multi-Temporal Latent Diffusion Transformers for Cloud Removal in Remote Sensing Images
Bibliographic record
Abstract
Cloud removal is an imperative pre-processing task for applications which extract land surface information from remote sensing images. While previous SOTA approaches to cloud removal have leveraged diffusion models, these methods operate in pixel space, and the feasibility of latent-space reconstruction techniques has been unexplored in this domain. In this work, we introduce multi-temporal latent diffusion transformers (MT-DiT), a novel framework that uses transformer-based diffusion in latent space to reconstruct cloud-covered regions. By compressing cloudy images into latent patch embeddings and integrating historical multi-temporal observations through a cross-attention conditioning mechanism, MT-DiT is able to capture rich spatiotemporal context from latent space representations. This design extends classifier-free guidance for diffusion transformers (DiT) to multi-temporal latent conditioning, enabling more detailed and consistent restoration of obscured land surfaces. Experiments on a publicly available dataset show that MT-DiT outperforms existing methods in key metrics, underscoring the advantages of latent diffusion model and transformer architectures for land surface reconstruction.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".