Production of Global Land Cover Data – GLCNMO2013
Bibliographic record
Abstract
Global land cover products have been created for global environmental studies by several institutions and organizations. The Global Mapping Project coordinated by the International Steering Committee for Global Mapping (ISCGM) has been periodically producing global land cover datasets asone of the eight basic global datasets. It has produced a new fifteen-second (approximately 500 m resolution at the equator) global land cover dataset – GLCNMO2013 (or GLCNMO version 3). This paper describes the method of producing GLCNMO2013. GLCNMO2013 has 20 land cover classes, and they were mapped by improved methods from GLCNMO version 2. In GLCNMO2013, five classes,which are urban, mangrove, wetland, snow/ice, and waterwere independently classified. The remaining 15 classes were divided into 4 groups and mapped individually by supervised classification. 2006 polygons of training data collected for GLCNMO2008 were used for supervised classification. In addition, about 3000 polygons of new training data were collected globally using Google Earth, MODIS Normalized Difference Vegetation Index (NDVI) seasonal change patterns, existing regional land cover maps, and existing four global land cover products. The primary data of this product were Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013. GLCNMO2013 was validated at 1006 sampled points. The overall accuracy of GLCNMO2013 was 74.8%, and the overall accuracy for eight aggregated classes was 90.2%. The accuracy of the GLCNMO2013 was not improved compared with the GLCNMO2008 at heterogeneous land covers. It is necessary to prepare the training data for mosaic classes and heterogeneous land covers for improving the accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".