Combined Detection and Segmentation of Archeological Structures from LiDAR Data Using a Deep Learning Approach

Abstract

Until recently, archeological prospection using LiDAR data was based mainly on expertbased and time-consuming visual analyses. Currently, deep learning convolutional neural networks (deep CNN) are showing potential for automatic detection of objects in many fields of application, including cultural heritage. However, these computer-vision based algorithms remain strongly restricted by the large number of samples required to train models and the need to define target classes before using the models. Moreover, the methods used to date for archaeological prospection are limited to detecting objects and cannot (semi-)automatically characterize the structures of interest. In this study, we assess the contribution of deep learning methods for detecting and characterizing archeological structures by performing object segmentation using a deep CNN approach with transfer learning. The approach was applied to a terrain visualization image derived from airborne LiDAR data within a 200 km² area in Brittany, France. Our study reveals that the approach can accurately (semi-)automatically detect, delineate, and characterize topographic anomalies, and thus provides an effective tool to inventory many archaeological structures. These results provide new perspectives for large-scale archaeological mapping.

Applying automated object detection in archaeological practice: A case study from the southern Netherlands

“…This process might unintentionally rein- The detection of all archaeological objects of interest within an certain area would require either many different models, a model detecting a multitude of classes or a model that detects a more general class of 'archaeological anomalies'. Recently, attempts at developing the latter have been made (Guyot et al, 2021). However, questions remain how applicable these models are in complex terrain, for example, the Veluwe, where many objects of confusion are present, and how useful such a catch-all category would be in terms of both archaeological research and heritage management (see Trier et al, 2019).…”

“…A major advantage of CNNs is the possibility to use transfer‐learning (Razavian et al., 2014), where a CNN is pre‐trained on a large, generic dataset and subsequently is fine‐tuned on a small, specific dataset. In archaeology, transfer‐learning has been successfully implemented on different types of remotely sensed data from Europe (Bonhage et al., 2021; Gallwey et al., 2019; Guyot et al., 2021; Kazimi et al., 2019; Trier et al., 2019; Verschoof‐van der Vaart & Lambers, 2019; Verschoof‐van der Vaart et al., 2020; Verschoof‐van der Vaart & Landauer, 2021; Zingman, 2016; Zingman et al., 2016) and further abroad (Bundzel et al., 2020; Caspari & Crespo, 2019; Somrak et al., 2020; Soroush et al., 2020; Trier et al., 2018, 2021). To date these approaches are generally tested in an (ideal) experimental setting but have not been applied in different contexts or ‘in the wild’, that is, incorporated in archaeological prospection, although the latter is the main aim of most initiatives (see Trier et al., 2019).…”

Machine Learning Arrives in Archaeology

“…The increasing availability of large-scale lidar, satellite, and aerial imagery on local, regional, and national scales, however, is transforming archaeology around the globe—particularly the searching and mapping of archaeological sites (Figure 2). ML algorithms can be used to process the geospatial data in the search for sites in diverse environments (Bonhage et al 2021; Caspari and Crespo 2019; Davis 2019; Davis, DiNapoli, et al 2020; Davis, Seeber, et al 2020; Evans and Hofer 2019; Guyot et al 2018, 2021; Orengo et al 2020; Soroush et al 2020; Thabeng et al 2019; Trier et al 2018, 2019; Verschoof-van der Vaart and Lambers 2019; Verschoof-van der Vaart et al 2020).

FIGURE 2.An illustrative fictional example of how machine learning may be applied to feature identification in geospatial data and the reconstruction of a site.

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GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

“…It has experimented with geospatial data/images (satellite, aerial, lidar), texts, categorical tableau data, point clouds, and other datasets. For instance, one can consider some indicative examples such as the work that has been done on bone classification [1], remote sensing archaeology [2][3][4][5][6][7][8][9][10][11][12], geophysical prospection [13][14][15][16][17], detection of objects in paintings [18], classification of pottery [19], and the 3D reconstruction of heritage buildings [20]. The main reason behind this growing trend, which has been noticed in the last five years in all scientific domains, underlies the nuisance generated when dealing with multivariate analysis of high-volume datasets, which are challenging to process and interpret.…”