Inter- & Intra-City Image Geolocalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 m<sup>2</sup>. 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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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) | 0.001 | 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 it