Free Access to Landsat Imagery
Bibliographic record
Abstract
![Figure][1] Free image. This Landsat 5 image of the southeastern corner of the Black Sea is part of the general U.S. archive that will be accessible for free under the new USGS policy. CREDIT: BOSTON UNIVERSITY CENTER FOR REMOTE SENSING We are entering a new era in the Landsat Program, the oldest and most venerable of our Earth-observing satellite programs. With little fanfare, the U.S. Geological Survey (USGS) has begun providing imagery for free over the Internet. Throughout the history of the Landsat Program, the cost and access to imagery has always limited our ability to study our planet and the way it is changing. Beginning with a pilot program to provide “Web-enabled” access to Landsat 7 images of the United States that were collected between 2003 and this year, the USGS now plans to provide top-quality image products for free upon request for the entire U.S. archive, including over 2 million images back to Landsat 1 (1972) [for details and schedules, see ([1][2])]. The release by NASA and the USGS in January 2008 of a new Landsat Data Distribution Policy ([2][3]) was a key step to this goal. Free imagery will enable reconstruction of the history of Earth's surface back to 1972, chronicling both anthropogenic and natural changes during a time when our population doubled and the impacts of climate change became noticeable. The Landsat Science Team: 1. 1.[↵][4]USGS Technical Announcement ([http://landsat.usgs.gov/images/squares/USGS\_Landsat\_Imagery_Release.pdf][5]). 2. 2.[↵][6]Landsat Missions ([http://ldcm.usgs.gov/pdf/Landsat\_Data\_Policy.pdf][7]). [1]: pending:yes [2]: #ref-1 [3]: #ref-2 [4]: #xref-ref-1-1 View reference 1. in text [5]: http://landsat.usgs.gov/images/squares/USGS_Landsat_Imagery_Release.pdf [6]: #xref-ref-2-1 View reference 2. in text [7]: http://ldcm.usgs.gov/pdf/Landsat_Data_Policy.pdf
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.536 | 0.611 |
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".