GANInSAR: Deep Generative Modeling for Large-Scale InSAR Signal Simulation
Bibliographic record
Abstract
Interferometric Synthetic Aperture Radar (InSAR) technology is widely used to create digital elevation models and measure dynamics on the earth's surface, including monitoring ground displacements. The lack of or limited-collected ground-truth data, however, often poses a bottleneck in validating the research outcome, particularly at high precision and resolution levels. To mitigate the gap, we introduce a new Deep Generative Model (DGM) for the simulation of linear deformation rate maps. We demonstrate that our adversarial DGM architecture with carefully designed pre-processing and post-processing modules performs well for InSAR deformation signal synthesis, even when limited data is available. We also introduce a dimensionality reduction method, based on the distance between the real-world and generated image feature vectors, to address the lack of quantitative evaluation for data simulation. Furthermore, we introduce a hybrid evaluation metric integrating quantitative and qualitative measures, which is more intuitive than the existing methods and makes it easier for domain experts to participate in the evaluation. We compare the results of our model with established methods. The comparison result illustrates the superior performance of our proposed method.
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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 | 0.000 | 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 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".