Virtual cultural landscapes: Geospatial visualizations of past environments
Bibliographic record
Abstract
Abstract Recent advances in spatial and remote sensing technology have led to new methods in archaeological site identification and reconstruction, allowing archaeologists to investigate landscapes and sites on multiple scales. These remotely conducted surveys create virtual cultural landscapes and seascapes that archaeologists and the public interact with and experience, often better than traditional maps. Our study examines landscape reconstruction and archaeological site classifications from a phenomenological and human behavioural ecology (HBE) perspective. HBE aims to reconstruct how humans interacted with these places as part of their active and passive decision making. Through temporal reconstructions, archaeologists and others can experience and interpret past landscapes and subtle changes in cultural land‐ and seascapes. Here, we evaluate the use of remotely sensed data (lidar, satellite imagery, sonar, radar, etc.) for developing virtual cultural landscapes while also incorporating Indigenous perspectives. Our study compares two vastly different landscapes and perspectives: a seascape in coastal Alaska, USA, and a neotropical jungle in Belize, Central America. By incorporating ethnographic accounts, oral histories, Indigenous traditional knowledge and community engagement, archaeologists can develop new tools to understand decisions made in the past, especially pertaining to settlement selection and resource procurement. These virtual reconstructions become cognitive images of a possible place that the observer experiences. Virtual cultural landscapes allow archaeologists to reproduce landscapes that may otherwise be invisible and present them to different publics. These processes elucidate how landscapes changed over time based on human behaviours while simultaneously allowing archaeologists to engage with Indigenous communities and the public in the protection of prehistoric and historic sites and sacred spaces through cultural heritage management.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".