Automated backdating of transportation networks with Landsat imagery
Bibliographic record
Abstract
Unfortunately, many GIS layers depicting transportation networks do not provide information on the construction year of each line segment in the network. This poses a serious problem when the GIS layer is used as input to historic analyses investigating the growth of the human footprint in an area still being developed, since there is no way of finding out what features already existed at each time lag of the period under analysis. Here we assess the possibility of backdating (a.k.a. retro-fitting) a road network to a reference year (by removing features in the GIS layer whose ground counterparts were not yet built then), using (1) a single Landsat image from the reference year (single date approach); and (2) the latter plus another from a more recent year (multi-date approach). We provide succinct information on the study area, input RS and GIS data, methods, and results; and conclude that full automation of this task is feasible.
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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) | 0.000 | 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".