Fanar M. Abed

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multiple RS datasets to overcome limitations and produce comparatively detailed outcomes. However, there are still knowledge gaps in examining the effectiveness of these RS approaches in enhancing the detection of archaeological remains/areas. Thus, this review paper is likely to deliver valuable comprehension for archaeological studies to fill knowledge gaps and further advance exploration of archaeological areas/features using RS along with DL approaches.

Remote sensing data are increasingly being used in digital archaeology for the potential non-invasive detection of archaeological remains. The purpose of this research is to evaluate the capability of standalone (LiDAR and aerial photogrammetry) and integration/fusion remote sensing approaches in improving the prospecting and interpretation of archaeological remains in Cahokia’s Grand Plaza. Cahokia Mounds is an ancient area; it was the largest settlement of the Mississippian culture located in southwestern Illinois, USA. There are a limited number of studies combining LiDAR and aerial photogrammetry to extract archaeological features. This article, therefore, combines LiDAR with photogrammetric data to create new datasets and investigate whether the new data can enhance the detection of archaeological/ demolished structures in comparison to the standalone approaches. The investigations are implemented based on the hillshade, gradient, and sky view factor visual analysis techniques, which have various merits in revealing topographic features. The outcomes of this research illustrate that combining data derived from different sources can not only confirm the detection of remains but can also reveal more remains than standalone approaches. This study demonstrates that the use of combination remote sensing approaches provides archaeologists with another powerful tool for site analysis.