Abstract
This contribution examines the potential of object-based image analysis (OBIA) for archaeological predictive modeling starting from elevation data, by testing a ruleset for the location of “control places” on two test areas in the Alpine environment (northern Italy). The ruleset was developed on the western Asiago Plateau (Vicenza Province, Veneto) and subsequently re-applied (semi)automatically in the Isarco Valley (South Tirol). Firstly, we considered the physiographic, climatic, and morphological characteristics of the selected areas and we applied 3 DTM processing techniques: Slope, local dominance, and solar radiation. Subsequently, we employed an object-based approach to classification. Solar radiation, local dominance, and slope were visualized as a three-layer RGB image that was segmented with the multiresolution algorithm. The classification was implemented with a ruleset that selected only image–objects with high local dominance and solar radiation, but low slope, which were considered more suitable parameters for human occupation. The classification returned five areas on the Asiago Plateau that were remotely and ground controlled, confirming anthropic exploitation covering a time span from protohistory (2nd-1st millennium BC) to the First World War. Subsequently, the same model was applied to the Isarco Valley to verify the replicability of the method. The procedure resulted in 36 potential control places which find good correspondence with the archaeological sites discovered in the area. Previously unknown contexts were further controlled using very high-resolution (VHR) aerial images and digital terrain model (DTM) data, which often suggested a possible (pre-proto)historic human frequentation. The outcomes of the analysis proved the feasibility of the approach, which can be exported and applied to similar mountainous landscapes for site predictivity analysis.
Highlights
Predictive modeling is a process aimed at creating or choosing mathematical/statistical tools able to predict with accuracy the probability of an outcome [1,2]
The predictive model was designed in order toautomatically identify all control places present in the test area, and on the ridges of the Assa and the Astico valleys, that control numerous important communication routes connecting the lowland of Veneto with the Alpine area (Figure 3)
Looking at the results of the classification, it was soon apparent that the Bostel site is among the areas identified by the model (Figure 3, image–object E). This further confirms the vocation of the settlement as a control place, while on the other hand, this validates the efficiency of the predictive model itself because the site is the only known protohistoric hilltop settlement in the considered region
Summary
Predictive modeling is a process aimed at creating or choosing mathematical/statistical tools able to predict with accuracy the probability of an outcome [1,2] In archaeology, this usually implies the ability to envisage the location of ancient sites in a given area (terra incognita), based on a training set of known contexts (terra cognita) or on assumptions about the past human behavior [3]. This usually implies the ability to envisage the location of ancient sites in a given area (terra incognita), based on a training set of known contexts (terra cognita) or on assumptions about the past human behavior [3] These simulations can be classified as data-driven, i.e., founded on an inductive approach, or theory-driven, i.e., founded on a deductive approach [4].
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