Sampling based image splitting in large scale distributed computing of earth observation data
Bibliographic record
Abstract
With increasing amounts of spatial, spectral and temporal remote sensing data and heterogeneity of platforms, we have entered an era of big data in remote sensing research. Imagery now routinely exceeds the memory size of personal computers so splitting/distributing big remote sensing data becomes a necessary pre-processing step. Standard rectangle based splitting methods can distort existing geometric and topological information and lose features as images are split into tiles. To address these challenges, we propose a sampling based image splitting method, which models the dataset as a streaming service and splits the dataset with a Voronoi diagram. The streaming data is systematically sampled to initially select the seeds of a Voronoi diagram. Voronoi regions are then generated according to spatial and spectral distances using Fortune's sweepline algorithm [1]. We test the splitting method with AVIRIS imagery of North America in 2013 (courtesy of NASA/JPL-Caltech) to evaluate the ability to detect objects of our splitting method. For evaluation we employ the object-based classification method of Hay and Castilla [2]. In contrast to rectangle based splitting approaches, most polygon borders generated by our method are found to converge with object borders (e.g., trees, building, and roads). When deployed with MapReduce, our sampling based splitting method also helps balance the computation intensity between each computing node.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".