Land cover 2.0
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Other designConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.880
- Threshold uncertainty score
- 0.741
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
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 | 0.001 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.237 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- International Journal of Remote Sensing
- Topic
- Remote Sensing in Agriculture
- Field
- Environmental Science
- Canadian institutions
- Natural Resources CanadaUniversity of British ColumbiaCanadian Forest Service
- Funders
- Canadian Space AgencyNational Aeronautics and Space Administration
- Keywords
- Land coverRemote sensingAdvanced very-high-resolution radiometerEarth observationContext (archaeology)Thematic MapperLand information systemComputer scienceCover (algebra)Earth system scienceThematic mapSatelliteData scienceLand useSatellite imageryGeographyLand managementCartographyGeologyEngineering
- Has abstract in OpenAlex
- yes