Visualizing Land Cover and Land-Cover Change: A Review of Existing Methods and Remaining Challenges
Bibliographic record
Abstract
Over the past decades, Earth science data have dramatically increased and have been used to understand the Earth system. Land cover and land-cover change (LCLCC) data have been an integral part of monitoring the Earth’s surface, understanding environmental conditions, and managing resources. Visualizing LCLCC plays an important role in increasing the usability of LCLCC data and science for researchers and practitioners. However, visually communicating large, spatiotemporal LCLCC data sets, with different levels of complexity, to a variety of audiences presents a number of challenges. To explore ways to address this matter, this article provides background information on fundamental concepts and methods of data visualization. The authors review visualization methods found in LCLCC literature (2015–2023) and provide illustrative examples for a study domain in California, USA. They discuss challenges associated with developing LCLCC visualizations, with the focus on complex information in a single visualization. To address this challenge, the authors highlight data visualization approaches that aimed at simplifying the high-information content of LCLCC and improving land-cover science communication and the usability of LCLCC data.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".