Land cover 2.0
Bibliographic record
Abstract
Interest in knowing more about the Earth’s land cover and how it has changed over time motivated the mission and sensor design of early terrestrial remote sensing systems. Rapid developments in computer hardware and software in the last four decades have greatly increased the capacity for satellite data acquisition, downlink, dissemination, and end user science and applications. In 1992, Townshend reviewed the state of land cover mapping using Earth observation data at a pivotal point in time and in the context of years of research and practical experience with Landsat Thematic Mapper (TM), Satellite Pour l’Observation de la Terre (SPOT) High Resolution Visible (HRV) and Advanced Very-High-Resolution Radiometer (AVHRR) data, demonstrating the opportunities and information content possible with increased spatial, spectral, and temporal resolutions. Townshend characterized the state-of-the-art for land cover at that time, identified trends, and shared insights on research directions. Now, on the 25th anniversary of Townshend’s important work, given numerous advances and emerging trends, we revisit the status of land cover mapping with Earth observation data. We posit that a new era of land cover analysis – Land Cover 2.0 – has emerged, enabled by free and open access data, analysis ready data, high performance computing, and rapidly developing data processing and analysis capabilities. Herein we characterize this new era in land cover information, highlighting institutional, computational, as well as theoretical developments that have occurred over the past 25 years, identifying the key issues and opportunities that have emerged. We conclude that Land Cover 2.0 offers efficiencies in information generation that will result in a proliferation of land cover products, reinforcing the need for transparency regarding the input data and algorithms used as well as adoption, implementation, and communication of rigorous accuracy assessment protocols. Further, land cover and land change assessments are no longer independent activities. Knowledge of land change is available to inform and enrich land cover generation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.064 |
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".