Using remote sensing and aerial archaeology to detect pit house features in Worldview-2 satellite imagery. : A case study for the Bridge River archaeological pit house village in south-central British Columbia, Canada.
Bibliographic record
Abstract
It is well known that archaeological sites are important sources for understanding past human activity. However, those sites yet to be identified and further investigated are under a great risk of being lost or damaged before their archaeological significance is fully recognized. The aim of this research was to analyze the potential use of remote sensing and aerial archaeology techniques integrated within a geographic information system (GIS) for the purpose of remotely studying pit house archaeology. As pit house archaeological sites in North America have rarely been studied with a focus in remote sensing, this study intended to identify these features by processing very high resolution satellite imagery and assessing how accurately the identified features could be automatically mapped with the use of a GIS. A Worldview-2 satellite image of the Bridge River pit house village in Lillooet, south-central British Columbia, was processed within ArcGIS 10.1 (ESRI), ERDAS Imagine 2011 (Intergraph) and eCognition Developer 8 (Trimble) to identify spatial and spectral queues representing the pit house features. The study outlined three different feature extraction methods (GIS-based, pixel-based and object-based) and evaluated which method presented the best results. Though all three methods produced similar results, the potential for performing object-based feature extraction for research in aerial archaeology proved to be more advantageous than the other two extraction methods tested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".