The Use of Remotely Sensed Data in Rapid Rural Assessment
Bibliographic record
Abstract
This article discusses how analysis of remotely sensed data can be applied in rapid rural assessment and how its application can expand the spatial analysis of land-use/land-cover (LULC) change. It describes the methodological steps to carry out an LULC analysis based on Landsat Thematic Mapper image analysis under time and budget constraints. The article presents intra-and intercommunity comparisons of different LULC patterns. The discussion focuses on the trade-off between the desirable degree of land-cover class complexity, the level of class detail, and the required ground-truthing associated with each of these choices. The authors conclude that remotely sensed analysis can enhance short-term, low-budget fieldwork. Analysis of remotely sensed data can reduce costs before fieldwork by helping to inform where to concentrate data collection efforts, during fieldwork by extending spatial analysis to areas where accessibility is poor and that otherwise would not be included, and after fieldwork by improving the spatial and temporal scope of the analysis.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".