Emerging Applications of LiDAR / Airborne Laser Scanning in the Management of World Heritage Sites
Bibliographic record
Abstract
Remotely sensed data and imagery have revolutionized the way we understand archaeological sites and landscapes. LiDAR / airborne laser scanning (ALS) has been used to capture the often subtle topographic remnants of previously undiscovered sites even in intensely studied landscapes, and is rapidly becoming a key technology in survey projects with large extents and/or difficult terrain. This paper examines the practical application of this technology to archaeological heritage management, with special attention given to how ALS can support the World Heritage List nomination process and management of WHS archaeological sites and landscapes. It presents a number of examples from published ALS studies alongside case studies from projects undertaken by the authors at Cultural Site Research and Management and the Cultural Site Research and Management Foundation, Baltimore, Maryland, USA. The paper opens with a review of how ALS has been used at established World Heritage Sites, focusing on the Archaeological Ensemble of the Bend in the Boyne, Ireland, and the Angkor Archaeological Site in Cambodia. ALS applications for site prospection and demarcation, and viewshed analysis is explored in this section. Following this, we explore how ALS has been used to support two recent applications: the successfully nominated Monumental Earthworks at Poverty Point, USA and the recently nominated Orheiul Vechi Archaeological Landscape in Moldova. We propose that the detail offered by ALS data greatly strengthens nomination dossiers by emphasizing the outstanding universal value of sites, highlighting significant features and providing greater context to wider landscapes, and is particularly efficacious in delineating site boundaries for legal protection and long-term management. Finally, we conclude with a look at some of the practical considerations involved in the use of ALS, including access and training.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".