The Maya Landscape of El Infiernito, Chiapas, Mexico: Comparison of Occupied and Unoccupied Airborne LiDAR Mapping Systems
Bibliographic record
Abstract
Landscape-oriented approaches in archaeology have moved beyond site-based research to interpret how people have engaged with, modified, and constructed the environment and how the legacies of these activities continue to influence land use. In the Maya Lowlands, landscape archaeology is related to the analysis of settlement patterns, households, agricultural intensification, and water management. The increasing availability of LiDAR data has revolutionized the mapping of archaeological landscapes under vegetation, especially in tropical environments like the Maya Lowlands, but researchers still emphasize site-oriented settlement densities and infrastructure. Furthermore, the accessibility of drone-based LiDAR platforms has the potential to collect data across several seasons or years to facilitate change detection. In this paper, we compare three LiDAR datasets collected from 2018 to 2023, using both occupied and unoccupied airborne systems. The landscape surrounding the archaeological site of El Infiernito, Chiapas, Mexico near the Classic period (AD 250–800) dynastic capital of Piedras Negras, Guatemala was selected to compare these LiDAR datasets in the context of prior, extensive ground-based fieldwork. These data were used to interpret the built environment, land use, hydrology, landscapes of movement, and other infrastructure constructed and modified by several communities beginning in the Late Preclassic period (400 BC–AD 250) to the present. When used alongside systematic survey and ground verification, the combination of several LiDAR platforms to collect data across different seasons at El Infiernito enhanced the understanding of the spatial distribution of archaeological sites and features across the karst landscape.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".