Inter- & Intra-City Image Geolocalization
Bibliographic record
Abstract
Can a photo be accurately geolocated within a city from its pixels alone? While this image geolocation problem has been successfully addressed at the planetary- and nation-levels when framed as a classification problem using convolutional neural networks, no method has yet been able to precisely geolocate images within the city- and/or at the street-level when framed as a latitude/longitude regression-type problem. We leverage the highly densely sampled Streetlearn dataset of imagery from Manhattan and Pittsburgh to first develop a highly accurate inter-city predictor and then experimentally resolve, for the first time, the intra-city performance limits of framing image geolocation as a regression-type problem. We then reformulate the problem as an extreme-resolution classification task by subdividing the city into hundreds of equirectangular-scaled bins and train our respective intra-city deep convolutional neural network on tens of thousands of images. Our experiments serve as a foundation to develop a scalable inter- and intra-city image geolocation framework that, on average, resolves an image within 250 m2. We demonstrate that our models outperform SIFT-based image retrieval-type models based on differing weather patterns, lighting conditions, location-specific imagery, and are temporally robust when evaluated upon both past and future imagery. Both the practical and ethical ramifications of such a model are also discussed given the threat to individual privacy in a technocentric surveillance capitalist society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".