Data Reduction and Deep-Learning Based Recovery for Geospatial Visualization and Satellite Imagery
Bibliographic record
Abstract
The storage, retrieval, and distribution of data are some critical aspects of big data management. Data scientists and decision-makers often need to share large datasets and make decisions on archiving or deleting historical data to cope with resource constraints. A potential approach to mitigate such problems is to reduce big datasets into smaller ones, which will not only lower storage requirements but also allow light load transfer over the network. Carefully prepared data by removing redundancies, along with a machine learning model capable of reconstructing the whole dataset from its reduced version, can improve the storage scalability, data transfer, and speed up the overall data management pipeline. In this paper, we explore some data reduction strategies for big datasets, while ensuring that the data can be transferred and used ubiquitously by all stakeholders, i.e., the entire dataset can be reconstructed with high quality whenever necessary. Our approach guarantees a minimum of 75% data size reduction, where the reconstruction accuracy observed is as high as 98.75% on an average for geospatial meteorological data (e.g., soil moisture and albedo), and 99.09% for satellite imagery. We propose a novel variance based reduction technique that can further reduce the data size without losing the accuracy significantly, and adopt various deep learning approaches for high-quality 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".