LiDAR Applications in Archaeology: A Systematic Review

Landscape‐scale LiDAR‐based studies are becoming increasingly prevalent in archaeology, mainly focusing on detecting archaeological sites to create datasets for spatial analysis. However, the representativeness of these datasets in accurately reflecting the surviving distributions of archaeological sites has often been overlooked. This paper discusses issues of sampling and representativeness in LiDAR‐derived datasets, particularly within the scope of large‐scale landscape studies in Mediterranean contexts. Drawing insights from the Ancient Hillforts Survey, which analysed 15 296 km2 in south‐central Italy, the study examines the variability in the visibility of different site typologies in open‐source but low‐resolution LiDAR data. Through an examination of hillforts, platform farms, settlements, field systems, traces of Roman centuriation, and transhumance routes, the paper highlights significant variability in the identification and mapping within and across different site types. Recognizing the need to account for this variability in the development of spatial analysis, the paper discusses the use of sampling areas to address this variability. This approach aims to effectively mitigate potential biases in analysis, emphasizing the necessity for nuanced methodologies in interpreting LiDAR data for archaeological research.

Issues of Sampling and Representativeness in Large‐Scale LiDAR‐Derived Archaeological Surveys in Mediterranean Contexts

Fontana

2024

Automated Detection of Hillforts in Remote Sensing Imagery With Deep Multimodal Segmentation

Canedo,

Fonte,

Dias

et al. 2024

Recent advancements in remote sensing and artificial intelligence can potentially revolutionize the automated detection of archaeological sites. However, the challenging task of interpreting remote sensing imagery combined with the intricate shapes of archaeological sites can hinder the performance of computer vision systems. This work presents a computer vision system trained for efficient hillfort detection in remote sensing imagery. Equipped with an adapted multimodal semantic segmentation model, the system integrates LiDAR‐derived LRM images and aerial orthoimages for feature fusion, generating a binary mask pinpointing detected hillforts. Post‐processing includes margin and area filters to remove edge inferences and smaller anomalies. The resulting inferences are subjected to hard positive and negative mining, where expert archaeologists classify them to populate the training data with new samples for retraining the segmentation model. As the computer vision system is far more likely to encounter background images during its search, the training data are intentionally biased towards negative examples. This approach aims to reduce the number of false positives, typically seen when applying machine learning solutions to remote sensing imagery. Northwest Iberia experiments witnessed a drastic reduction in false positives, from 5678 to 40 after a single hard positive and negative mining iteration, yielding a 99.3% reduction, with a resulting F1 score of 66%. In England experiments, the system achieved a 59% F1 score when fine‐tuned and deployed countrywide. Its scalability to diverse archaeological sites is demonstrated by successfully detecting hillforts and other types of enclosures despite their typical complex and varied shapes. Future work will explore archaeological predictive modelling to identify regions with higher archaeological potential to focus the search, addressing processing time challenges.

Remote Sensing Analysis and LiDAR Experimenting in the Espique Valley (La Peza, Granada, Spain)

Bulnes,

Román Punzón,

Martín Civantos

2024

The Espique valley is a small space at the foot of the mountain, practically closed, which hides a rich archaeological heritage, not only of habitats but also of varied productive spaces. For some years now, MEMOLab UGR has been carrying out research that is now enriched with the contribution of LiDAR, with whose application we are experimenting with at a landscape scale but also with the precision of terrestrial tools for an integrated high‐resolution graphic documentation, which allows us to carry out analyses on the evolution of the settlement and the occupation and uses of this valley.