Preliminary Archeological Site Survey by UAV-Borne Lidar: A Case Study

Balsi, Marco; Esposito, Salvatore; Fallavollita, Paolo; Melis, Maria Grazia; Milanese, M

doi:10.3390/rs13030332

Cited by 29 publications

(15 citation statements)

References 11 publications

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“…However, the LiDAR data used in the present study have already been introduced in another article [1]. Drone-based ALS was tested, for example, in Norway in 2018 with promising results [13] and in Italy in 2021 [14]. However, more research is needed on drone-based ALS in archaeology.…”

Section: Resultsmentioning

confidence: 99%

The Hidden Cairns—A Case Study of Drone-Based ALS as an Archaeological Site Survey Method

2021

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Conducting archaeological site surveys is time consuming, and large sites may have many small features or structures that are difficult to locate and interpret. Vegetation cover and dense forest hide small structures, like cairns, while at the same time forest cover can cause problems for LiDAR tools. In this case study, drone-based ALS (airborne laser scanning) was tested as an archaeological site survey tool. The research site was complex and located partially in a forested area, which made it possible to evaluate how forest cover affects data. The survey methods used were rather simple: visual analysis, point density calculations in the forest area, and, for site interpretation purposes, digitizing observations and viewshed analysis. Using straightforward methods allowed us to evaluate the minimum time and skills needed for this type of survey. Drone-based ALS provided good results and increased knowledge of the site and its structures. Estimates of the number of cairns interpreted as graves more than doubled as a result of the high-accuracy ALS data. Based on the results of this study, drone-based ALS could be a suitable high-accuracy survey method for large archaeological sites. However, forest cover affects the accuracy, and more research is needed.

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Section: Resultsmentioning

confidence: 99%

The Hidden Cairns—A Case Study of Drone-Based ALS as an Archaeological Site Survey Method

2021

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“…Depending on the characteristics of the sites, certain representations can be helpful like elevation models obtained from stereoscopic images or the use of parts of the electromagnetic spectrum other than visible light like infrared or radio waves 15 , 16 . Light Detection and Ranging (LiDAR) is also becoming popular as it gives satisfactory high-resolution images, but it can be difficult to employ as it often requires to be mounted on some kind of airborne craft like drones 17 . The problem with these types of sources is that they might not be available for every location or not have a high enough resolution for the task at hand.…”

Section: Research Backgroundmentioning

confidence: 99%

A human–AI collaboration workflow for archaeological sites detection

Casini

Marchetti

Andrea

et al. 2023

Sci Rep

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This paper illustrates the results obtained by using pre-trained semantic segmentation deep learning models for the detection of archaeological sites within the Mesopotamian floodplains environment. The models were fine-tuned using openly available satellite imagery and vector shapes coming from a large corpus of annotations (i.e., surveyed sites). A randomized test showed that the best model reaches a detection accuracy in the neighborhood of 80%. Integrating domain expertise was crucial to define how to build the dataset and how to evaluate the predictions, since defining if a proposed mask counts as a prediction is very subjective. Furthermore, even an inaccurate prediction can be useful when put into context and interpreted by a trained archaeologist. Coming from these considerations we close the paper with a vision for a Human–AI collaboration workflow. Starting with an annotated dataset that is refined by the human expert we obtain a model whose predictions can either be combined to create a heatmap, to be overlaid on satellite and/or aerial imagery, or alternatively can be vectorized to make further analysis in a GIS software easier and automatic. In turn, the archaeologists can analyze the predictions, organize their onsite surveys, and refine the dataset with new, corrected, annotations.

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“…The resulting digital terrain models and digital surface models depict the surrounding reality in detail. These features determine the multidirectional use of airborne laser scanning in various fields of science, such as engineering solutions-calculation 3D displacements of bridges [3], 3D object detection along the road [4,5], building extraction [6,7], land cover change detection, and forest succession monitoring [8,9] for heterogeneous land use urban mapping [10], coastal monitoring [11,12], or archeological research [13,14].…”

Section: Literature Reviewmentioning

confidence: 99%

Using LiDAR System as a Data Source for Agricultural Land Boundaries

Borowiec

Marmol

2022

Remote Sensing

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In this study, LiDAR sensor data were used to identify agricultural land boundaries. This is a remote sensing method using a pulsating laser directed toward the ground. This study focuses on accurately determining the edges of parcels using only the point cloud, which is an original approach because the point cloud is a scattered set, which may complicate finding those points that define the course of a straight line defining the parcel boundary. The innovation of the approach is the fact that no data from other sources are supported. At the same time, a unique contribution of the research is the attempt to automate the complex process of detecting the edges of parcels. The first step was to classify the data, using intensity, and define land use boundaries. Two approaches were decided, for two test fields. The first test field was a rectangular shaped parcel of land. In this approach, pixels describing each edge of the plot separately were automatically grouped into four parts. The edge description was determined using principal component analysis. The second test area was the inner subdivision plot. Here, the Hough Transform was used to emerge the edges. Obtained boundaries, both for the first and the second test area, were compared with the boundaries from the Polish land registry database. Performed analyses show that proposed algorithms can define the correct course of land use boundaries. Analyses were conducted for the purpose of control in the system of direct payments for agriculture (Integrated Administration Control System—IACS). The aim of the control is to establish the borders and areas of croplands and to verify the declared group of crops on a given cadastral parcel. The proposed algorithm—based solely on free LiDAR data—allowed the detection of inconsistencies in farmers’ declarations. These mainly concerned areas of field roads that were misclassified by farmers as subsidized land, when in fact they should be excluded from subsidies. This is visible in both test areas with areas belonging to field roads with an average width of 1.26 and 3.01 m for test area no. 1 and 1.31, 1.15, 1.88, and 2.36 m for test area no. 2 were wrongly classified as subsidized by farmers.

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Preliminary Archeological Site Survey by UAV-Borne Lidar: A Case Study

Cited by 29 publications

References 11 publications

The Hidden Cairns—A Case Study of Drone-Based ALS as an Archaeological Site Survey Method

The Hidden Cairns—A Case Study of Drone-Based ALS as an Archaeological Site Survey Method

A human–AI collaboration workflow for archaeological sites detection

Using LiDAR System as a Data Source for Agricultural Land Boundaries

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