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.

With the increasing demands to use remote sensing approaches, such as aerial photography, satellite imagery, and LiDAR in archaeological applications, there is still a limited number of studies assessing the differences between remote sensing methods in extracting new archaeological finds. Therefore, this work aims to critically compare two types of fine-scale remotely sensed data: LiDAR and an Unmanned Aerial Vehicle (UAV) derived Structure from Motion (SfM) photogrammetry. To achieve this, aerial imagery and airborne LiDAR datasets of Chun Castle were acquired, processed, analyzed, and interpreted. Chun Castle is one of the most remarkable ancient sites in Cornwall County (Southwest England) that had not been surveyed and explored by non-destructive techniques. The work outlines the approaches that were applied to the remotely sensed data to reveal potential remains: Visualization methods (e.g., hillshade and slope raster images), ISODATA clustering, and Support Vector Machine (SVM) algorithms. The results display various archaeological remains within the study site that have been successfully identified. Applying multiple methods and algorithms have successfully improved our understanding of spatial attributes within the landscape. The outcomes demonstrate how raster derivable from inexpensive approaches can be used to identify archaeological remains and hidden monuments, which have the possibility to revolutionize archaeological understanding.

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.

Due to their importance for climate change monitoring, modelling and adaptation, vegetation structural properties including leaf area index (LAI) are designated essential climate variables (ECVs) by the Global Climate Observing System (GCOS). Terrestrial laser scanning (TLS), which rapidly acquires millions of three‐dimensional point measurements representing the physical environment, is an increasingly popular method for estimating these ECVs. To assess LAI from TLS data collected during leaf‐on conditions, a fundamental requirement is the classification of points as either leaves or wood. Existing intensity‐based leaf‐wood classification methods are confounded by natural variability in the reflectance of leaves and wood, bidirectional reflectance effects and the need for complex radiometric calibration, whilst geometric methods require high point density and are known to misclassify small branches and twigs. A novel leaf‐wood classification approach is presented that avoids these issues by exploiting the spectral transmittance properties of leaves and wood, which, at near‐infrared wavelengths, demonstrate much larger differences than for reflectance. The approach relies on classification of near‐infrared images collected by a co‐registered camera integrated with the TLS instrument and can be directly applied to the whole point cloud without segmentation. The technique is applied for leaf‐on estimation of LAI and wood area index (WAI) at a deciduous broadleaf forest site, and results are benchmarked against reference values derived from leaf‐off scans. Leaf‐on estimates of LAI and WAI demonstrated a small bias (RMSD ≤0.46, bias ≤0.17) but were not significantly different from reference values at the site level. The results provide evidence of the efficacy of the approach, and its use has the potential to reduce uncertainty in ECVs critical to climate change monitoring, modelling and adaptation.