Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement
Bibliographic record
Abstract
Accurate, up-to-date maps of and georeferenced data about human population distribution are essential for meeting the United Nations Sustainable Development Goals progress measures, for supporting real-time crisis mapping and response efforts, and for performing many demographic and economic analyses. In December 2014, Esri published the initial version of the World Population Estimate (WPE) image service to ArcGIS Online. The service represents a dasymetric footprint of human settlement at 250-meter resolution. It is global and contains an estimate of the 2013 population for each populated cell. In 2016 Esri published an additional image service representing the earth’s population in 2015 at 162-meter resolution. Esri’s WPE is produced by combining classified land cover data indicating predominantly built-up or agricultural locations with Landsat8 Panchromatic imagery, road intersections, and known populated places. The model detects where settlement is likely to exist beyond the areas classified as predominantly built up. The result is a global dasymetric raster surface of the footprint of settlement with a score of the likelihood of human settlement for each cell of the footprint. Population data are apportioned to this settlement likelihood surface by overlaying population counts in polygons representing census enumeration units or political units representing population surveys. This paper presents the method developed at Esri for producing the estimate of settlement likelihood.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".