Advice for archaeological survey with recent technologies

Abstract

Despite the technologies are now part of the archaeological discipline, they must not divert attention from archaeological issues and should be aimed at solving different historical and methodological questions, such the phases of a monument or innovative representation methods. The paper focuses the attention mainly on the use of range-data and image-based systems applied to the archaeological heritage, in order to highlight differences between the techniques and the related errors. Some considerations are necessary in order to find possible solutions for improving the quality and the accuracy of a survey project, considering the recent and innovative techniques adopted in archaeology. At the same time the authors provided, where possible, some solutions and suggestions for reducing the errors and checking the general quality of the work. Different experimentations have been made on some case studies that show how to manage technologies trying to reduce as much as possible the errors in the different phases of the survey pipeline. A specific part has been dedicated to the photogrammetric process from drones compared to traditional acquisitions, usually performed with aluminium poles, and the common errors in the representation of the archaeological excavations.

Three-Dimensional Modeling and Non-Invasive Diagnosis of a Huge and Complex Heritage Building: The Patriarchal Basilica of Santa Maria Assunta in Aquileia (Udine, Italy)

“…On these, all others were recorded with both manual and automatic point recognition systems. From the laser point clouds, second-order points were recorded to integrate data derived from architectural and aerial photogrammetry of the exterior [63]. Differences in resolution between the two systems resulted in increased registration error between the two parts.…”

Combined Use of 3D Metric Survey and GPR for the Diagnosis of the Trapezophoros with Two Griffins Attacking a Doe of Ascoli Satriano (Foggia, Italy)

“…Mesh production in which the point cloud was transformed onto a surface consisting of 380,785 triangulated dots (Figure 2c-e). In order to recognize and fit discontinuities on the model, the "multiresolution model" routine based on automated algorithms was used [54]. Furthermore, the "optimization method" and the "decimation filter" were applied for rearranging and smoothing, respectively, the nodes of the triangles and for simplifying the model and generating a multiresolution model.…”