Moderate resolution time series data management and analysis: automated large area mosaicking and quality control
Bibliographic record
Abstract
The Canada Centre for Remote Sensing1 (CCRS) maintains national-scale Long Term Satellite Data Records (LTSDRs) as an essential component of Earth Observation (EO) based land surface monitoring. The CCRS LTSDR framework provides long-term capability to generate, archive and provide access to value-added satellite data and thematic products addressing various land surface monitoring needs of the Government of Canada. For many years, coarse-resolution LTSDRs supported basic land-cover and landuse information needs over large areas. While these LTSDRs, with pixel sizes between 250m and 1000m, are important for ongoing long-term time series analysis, increasingly there is an opportunity to use greater spatial resolution data to more effectively address monitoring and assessment of both anthropogenic and natural land surface changes. Until recently, cost and availability limited the usefulness of medium resolution (~30m pixel size) EO data for such analyses. Then, in 2009, the United States Geological Survey made Landsat data freely available. The potential for medium-resolution time series monitoring has been further strengthened by Landsat-8 and the pending launch of ESA's Sentinels. For this potential to be realized, new methods and algorithms are required to extract and analyse information from the medium resolution data, such as Landsat Time Series data, to monitor aspects of land surface dynamics. CCRS' new medium resolution LTSDR framework, the Time Series Data Management and Analysis System (TSDMAS), generates and manages value-added LTSDRs based on the TM, ETM and OLI sensors on board the Landsat 5, 7 and 8 missions, respectively. An overview of the TSDAMS system, and the algorithms implemented therein, will be presented. A new value-added data product will also be presented: a Top of Atmosphere Reflectance Coverage of Canada, at 30 m spatial resolution. This circa 2010 product has been generated by the TSDAMS from data acquired by the TM and ETM sensors. Product generation, quality control, and characteristics of the underlying dataset will be described. By providing readily available, national-scale Landsat data products, of "research quality", CCRS TSDMAS harnesses the potential of medium resolution EO data, and can exponentially increase the downstream generation and use of medium resolution land surface information products. The new system and example data product which are described were designed to assist government agencies, the scientific community, natural resources managers and non-governmental groups engaged in land cover mapping, and the generation of geophysical and biophysical products for the assessment of surface dynamics at national and regional scales.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".