Development of an Open-Source Toolbox for the Analysis and Visualization of Remotely Sensed Time Series
Bibliographic record
Abstract
The GEONETCast data-dissemination system delivers free multi-source raw satellite images and processed products to users worldwide; from these data, users can construct long time series to study dynamic phenomena. To explore these dynamics, using an animation with few controls is common practice. But animations easily produce information overload leading to change blindness, a problem that can be addressed in various ways. We present a combination of analytical and visual functionalities to better support visual exploration of animated time series. Analytical pre-processing functions include slicing and tracking of objects of interest. Results of the slicing and the tracking are input to the visualization environment, which is further enriched by tools to make various time, attribute, and area selections and by options to visually enhance selections relative to their surroundings, visualize the path of moving objects, and multiple layers. The resulting toolbox is dedicated to visual exploration and analysis of dynamic phenomena in time series. A case study demonstrates, with a use scenario, how it works. Early exposure of some visualization functions to users has already led to improvements, but more extensive testing will follow after further enrichment of the toolbox. Directions of future research are described.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.018 |
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".