ARISGAN: Extreme Super-Resolution of Arctic Surface Imagery using Generative Adversarial Networks
Bibliographic record
Abstract
This research explores the application of generative artificial intelligence, specifically the novel ARISGAN framework, for generating high-resolution synthetic satellite imagery in the challenging arctic environment. The study addresses the crucial need for realistic and high-resolution surface imagery in the Arctic, vital for applications ranging from satellite retrieval systems to the well-being and safety of Inuit populations relying on detailed surface observations. The ARISGAN framework combines dense block, multireceptive field, and Pix2Pix architecture, showcasing promising results that surpass existing state-of-the-art models across diverse tasks and metrics. Land-based imagery super-resolution exhibits superior metrics in comparison to sea-based imagery across multiple models. This research contributes to the advancement of Earth Observation in polar regions by introducing a framework that combines advanced image processing techniques and a well-designed architecture. The ARISGAN framework's effectiveness in outperforming existing models underscores its potential for generating perceptually valid high-resolution arctic surface imagery. The study concludes with a discussion on identified limitations and proposes avenues for future research, emphasizing the importance of addressing challenges in temporal synchronicity, multi-spectral image analysis, pre-processing, and quality metrics. The findings encourage further refinement of the ARISGAN framework, ultimately advancing the quality and availability of high-resolution satellite imagery in the Arctic.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".