Global Monitoring with the <i>Atlas of Urban Expansion</i>
Bibliographic record
Abstract
United Nations Sustainable Development Goal 11, “Sustainable Cities and Communities,” makes information about cities’ areas, their populations, and their change over time, essential inputs to the goal's reporting requirements. The key charge of indicator 11.3.1 is to measure change in land consumption per capita over time in cities, to understand whether it is increasing, decreasing, or stable. The Atlas of Urban Expansion applied remote sensing and spatial analysis techniques to summarize global change in land consumption per capita using a sample-based approach. It revealed that land consumption per capita significantly increased on average in cities over the 1990–2000 and 2000–2014 periods. A relatively new publicly available global dataset of built-up area and population, the Global Human Settlements Layer (GHSL), now allows for the study of all cities on earth. Accuracy assessments of the Atlas and GHSL land cover classifications revealed encouraging similarities in overall accuracy but differences in each dataset's ability to correctly identify the built-up and open space classes individually. Before adopting global grids to inform indicator 11.3.1, we must be able to explain anomalous population density values in over one-quarter of cities resulting from a first pass at automated global settlement mapping .
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
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) | 0.027 | 0.000 |
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 teacher head, 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".