A Right Not to Be Mapped? Augmented Reality, Real Property, and Zoning
Bibliographic record
Abstract
The digital mapping applications underlying augmented reality have strong public benefits but can also have unappreciated effects on real property. In recent litigation on Pokémon Go, an enhanced digital mapping application in which players participate in a digital scavenger hunt by visiting real world locations, homeowners alleged that the augmented reality application harmed their residential properties by increasing the number of people in their residential areas. However, neither the existing laws on intellectual property nor those for real property are designed to address these types of harms. On the one hand, real property torts, such as nuisance and trespass, on which the homeowners relied, are ill-suited to address harms from a digital application as they are based on a right to exclude and consent. On the other hand, intellectual property laws have not focused on harms that could result from the intersection of intellectual property rights and real property. If it were to be framed anew, the basis of the homeowners’ claims would be most analogous to asserting “a right not to be mapped.” However, there is not yet a “right not to be mapped” in law, and there are compelling reasons for the law not to create one. We recommend three alternative mechanisms to regulate the relationship between augmented reality and real property. We recommend the application of zoning principles as a legal mechanism designed for location-sensitive regulation, which can balance the concerns of individual real property owners, as well as the larger context of community and city interests, and be adapted to innovative technologies such as augmented reality. Additionally, we suggest that catalogues of augmented reality applications be created to support zoning decisions and to provide public notice. We also consider the possibility of licensing schemes with micropayments for real properties affected by augmented reality.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".