The use of L-band SAR Derived Soil Moisture Data in Protecting Critical Infrastructure
Bibliographic record
Abstract
Summary From collapsing dams to failing embankments, disaster can strike when engineers fail to account for the presence of water in soil. ASTERRA addresses this risk by detecting underground soil moisture as deep as 3.0 metres below ground using patented algorithms combined with AI on satellite Synthetic Aperture Radar (SAR) data. From tailings dams to waste mineral tips, road and rail infrastructure, this method can identify damage and locate potential points of failure, allowing preventative maintenance to be directed to where it is needed most, and crucially before the onset of failure can occur. This proprietary methodology was originally used in the search for water on Mars and has since been adapted to monitor soil moisture around critical infrastructure on Earth from orbit. The process uses data from commercial satellites equipped with L-Band SAR, which can penetrate through clouds, vegetation and soil. SAR data is analysed using a patented algorithm before delivering hard intelligence to planners, engineers, and policymakers. This allows decision-makers to make data-informed choices about the repair, maintenance, and long-term planning of above and below-ground infrastructure. This paper highlights the benefits of SAR data as a means of remotely monitoring soil moisture content and thereby safeguarding critical infrastructure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".