A RADARSAT-2 polarimetric multi-incidence angle analysis over archaeological sites. The ancient UNESCO city of Samarra (Iraq)
Bibliographic record
Abstract
The present is part of a PhD project carried out between the University of Rome “Sapienza” and the University of Rennes1. This work has as goal the detection of archaeological buried remains and the monitoring of the external ones. \nThe archaeological site taken into account for this purpose is the area of the ancient octagonal city funded by Harun al-Rashid: al-Qadisiyya. This city, located in the southern part of the Samarra territory (Longitudes 43°45’- 43°51’; Latitudes 34°25’-34°05’ for a total extension of 15058 hectares), was abandoned unfinished when the caliph moved to Raqqa (Syria) in 796 A.D. Bigness of the structures and extensive excavation not yet occurred in that zone (as for the remaining 80% of the whole archaeological zone – 45km x 6km extension), the unstable political situation and agricultural expansion threats (that let the city of Samarra be inscribed in the UNESO list of sites in danger since 2007) gave us a reason more to investigate this area. \n \nThe study was carried out with four fine quad-pol imagery of the Canadian satellite RADARSAT-2, launched in December 2007. The images were scheduled and provided by VigiSat, in the frame of the GIS BRETEL and processed with the PolSARpro software. However C-band lower capability of penetration compared to ALOS PALSAR L-band, the choice of this satellite is due to its higher spatial resolution compared to the PALSAR one, whose data were employed in a previous study. Thanks to the higher spatial resolution and the location of the site in a semi desert area, we succeeded in balancing a probable lower waves penetration. \nOur analysis focused on four polarimetric images, two with a 23° incidence angle and two with a 45° incidence angle, acquired in different moments of the year 2012. The difference between the angles was motivated, respectively, by the possibility of a higher penetration of the microwaves in the ground and by the higher possibility of double bounce response in the case of presence of buried structures. The time spacing, on the other hand, allowed a temporal analysis over different months of the same year accompanied by meteorological condition available on the web for the zone. \nThe potentiality of this SAR research for archaeology is well known, in particular for those areas of the Globe where surveys in situ are not allowed because of political instability (as in the case of Samarra), or for those zones in which a cloud cover is always present and where optical satellites cannot acquire as radar does in any kind of illumination and in any sky coverage. As known there are still some limitations due to several natural factors (condition in soil humidity is the most important) and due to technical aspects (spatial resolution, inappropriate wave-band), that we hope will be settled in the near future.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".