This paper focuses on some spatial analyses performed on the dataset collected from the “Riparo Bombrini” in the Balzi Rossi archaeological area (Liguria, Italy). Documented during several campaigns using non-digital techniques, the excavation area has been surveyed in 2017 using a completely open source SFM photogrammetry operating chain (Python Photogrammetry Toolbox GUI for the point cloud computing and Meshlab for the mesh creation) and all the paper works relating to the positions of the findings has been digitized in a spreadsheet (LibreOffice Calc) and then imported into a GIS environment (QGIS). The positioning of all the findings in a GIS allowed us, for the first time since the beginning of the project, to start planning our postexcavation spatial analyses: the excavation area has been divided into a 10 cm squares grid and a presence/absence raster has been created (the cell value ranging from 0 to 18 findings). A second, and more appealing, approach tested has been the density analysis one: first a set of raster has been interpolated using the Nearest Neighbour algorithm for each archaeological horizon and then the KDE algorithm has been applied to the same dataset to create a second set of raster. The comparison between the two sets of raster clearly shows how the KDE algorithm (available both in ArcGIS and in QGIS) gives a more immediate visual perception of the different clusters of findings. Since the excavation is still on-going it is too early for any other analysis but the simple test of the methodology, but the ability shown to highlight any clustering of our findings is more than promising.

Il rilievo del riparo Bombrini: tecniche di fotogrammetria SFM ed analisi di densità per la gestione 3D di un contesto di scavo

Cristiano Putzolu
;
Fabio Negrino;
2023

Abstract

This paper focuses on some spatial analyses performed on the dataset collected from the “Riparo Bombrini” in the Balzi Rossi archaeological area (Liguria, Italy). Documented during several campaigns using non-digital techniques, the excavation area has been surveyed in 2017 using a completely open source SFM photogrammetry operating chain (Python Photogrammetry Toolbox GUI for the point cloud computing and Meshlab for the mesh creation) and all the paper works relating to the positions of the findings has been digitized in a spreadsheet (LibreOffice Calc) and then imported into a GIS environment (QGIS). The positioning of all the findings in a GIS allowed us, for the first time since the beginning of the project, to start planning our postexcavation spatial analyses: the excavation area has been divided into a 10 cm squares grid and a presence/absence raster has been created (the cell value ranging from 0 to 18 findings). A second, and more appealing, approach tested has been the density analysis one: first a set of raster has been interpolated using the Nearest Neighbour algorithm for each archaeological horizon and then the KDE algorithm has been applied to the same dataset to create a second set of raster. The comparison between the two sets of raster clearly shows how the KDE algorithm (available both in ArcGIS and in QGIS) gives a more immediate visual perception of the different clusters of findings. Since the excavation is still on-going it is too early for any other analysis but the simple test of the methodology, but the ability shown to highlight any clustering of our findings is more than promising.
2023
Cristiano Putzolu, Fabio Negrino, Julien Riel-Salvatore
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/939313
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