UAV LiDAR Based Approach for the Detection and Interpretation of Archaeological Micro Topography under Canopy—The Rediscovery of Perticara (Basilicata, Italy)

Abstract

This paper deals with a UAV LiDAR methodological approach for the identification and extraction of archaeological features under canopy in hilly Mediterranean environments, characterized by complex topography and strong erosion. The presence of trees and undergrowth makes the reconnaissance of archaeological features and remains very difficult, while the erosion, increased by slope, tends to adversely affect the microtopographical features of potential archaeological interest, thus making them hardly identifiable. For the purpose of our investigations, a UAV LiDAR survey has been carried out at Perticara (located in Basilicata southern Italy), an abandoned medieval village located in a geologically fragile area, characterized by complex topography, strong erosion, and a dense forest cover. All of these characteristics pose serious challenge issues and make this site particularly significant and attractive for the setting and testing of an optimal LiDAR-based approach to analyze hilly forested regions searching for subtle archaeological features. The LiDAR based investigations were based on three steps: (i) field data acquisition and data pre-processing, (ii) data post-processing, and (iii) semi-automatic feature extraction method based on machine learning and local statistics. The results obtained from the LiDAR based analyses (successfully confirmed by the field survey) made it possible to identify the lost medieval village that represents an emblematic case of settlement abandoned during the crisis of the late Middle Ages that affected most regions in southern Italy.

Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines

“…Sevara et al [29] compared semi-supervised object-based and pixelbased classification approaches to identify archaeological features from ALS datasets. Some recent studies rely or are partially based on machine learning approaches for archaeological object detection [30]- [32]. More recently, Deep Learning methods, e.g.…”

“…Furthermore, OBIA-based methods often have the disadvantage that several parameters or thresholds are included in the detection and classification procedure (see e.g. [27], [28], [32]).…”

UAV-Based Remote Sensing for Detection and Visualization of Partially-Exposed Underground Structures in Complex Archaeological Sites

“…Therefore, TLS is often preferred for smaller, unwooded areas as well as architectural remains. Considering this, the use of LiDAR units onboard UAVs is becoming an interesting concept for the mapping of archaeological sites [27][28][29].…”

“…Less semantic information. [28,30] (a) (b) In spite of the above characteristics of LiDAR data, the detection of underground structures (including cisterns) from point clouds remains a challenging task, especially when dealing with complex archaeological sites exhibiting steep slopes and dense vegetation [32][33][34]. The detection of cisterns is quite important for some archaeological sites, especially those in areas with limited access to fresh water.…”

UAV LiDAR in coastal environments: Archaeological case studies from Tierra del Fuego, Argentina, and Vega, Norway

“…Although the use of UAV LiDAR in archaeology remains limited compared with conventional LiDAR, it has been successfully employed to detect archaeological features under tree canopies in various environments across the world. This includes building foundations and field systems in Hawaii (Casana et al, 2021; McCoy et al, 2022), building features from a deserted village in Italy (Masini et al, 2022), deserted settlements in Spain (Monterroso‐Checa et al, 2021), grave mounds and charcoal production sites in Norway (Risbøl & Gustavsen, 2018), graves and clearance cairns in Finland (Roiha et al, 2021), building features and field systems in Mexico (Schroder et al, 2021), mapping historical conflict landscapes in Germany (Storch et al, 2022), an ancient walled settlement in Peru (VanValkenburgh et al, 2020) and mounds and building foundations in China (Zhou et al, 2020). Most of these projects involved field verifications of LiDAR identifications or evaluations of previous field surveys by employing LiDAR mapping.…”