Computer vision models for comparing spatial patterns: understanding spatial scale
Bibliographic record
Abstract
Comparison of landscapes and patterns is a long-standing challenge in spatial analysis research. Recently, new models and tools developed for non-geographic image data are being used to study geographic problems involving classification or prediction. Specifically, computer vision models and artificial neural networks have been deployed in an ever-growing number of geographical analyses. In this paper, we review the use of these models in geographical analysis, focusing on the representation and comparison of spatial patterns. We review artificial neural networks and provide semantic linking across domains using similar model constructs through the lens of scale. We note that scale, a contextual element in geographical research, is typically considered a model parameter in computer vision. Scale impacts both computer vision techniques and traditional pixel-based or object-oriented analysis, yet computer vision methods such as CNNs are relatively robust to small-scale variations due to their capability to learn multiscale features via spatial filtering and the formation of scale-space tensors across layers. Parameterization of computer vision models to represent multiscale patterns however remains ad hoc. A typology of scales, therefore, provides a framework for mapping model constructs to develop guidelines for parameterizing and evaluating computer vision models in a geographic context.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".