Machine learning approaches to Landsat change detection analysis
Bibliographic record
Abstract
The Landsat mission has captured images of the Earth’s surface for over 50 years, and the data have enabled researchers to investigate a vast array of different change phenomena using machine learning models. Landsat-based monitoring research has been influential in geography, forestry, hydrology, ecology, agriculture, geology, and public health. When monitoring Earth’s surface change using Landsat data and machine learning, it is essential to consider the implications of the size of the study area, specifics of the machine learning model, and image temporal density. We found that there are two general approaches to Landsat change detection analysis with machine learning: post-classification comparison and sequential imagery stack approaches. The two approaches have different advantages, and the design of an appropriate type of Landsat change detection analysis depends on the task at hand and the available computing resources. This review provides an overview of different Landsat change detection approaches using machine learning, outlines a framework for understanding the relevant considerations, and discusses recent developments such as generative artificial intelligence, explainable machine learning, and ethical analysis considerations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".