Airborne laser scanning raster data visualization

Kokalj, Žiga; Hesse, Ralf

doi:10.3986/9789612549848

Cited by 121 publications

(124 citation statements)

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“…While there are other DTM visualizations (Kokalj & Hesse, ) that could have been used instead of, or combined with, the local relief model, for the present study the simplified local relief model (SLRM), replicated for red, green and blue channels, is used. The SLRM, as a normalization of the topographic data that removes the influence of absolute elevation, seems to have some advantages for the present study.…”

Section: Methodsmentioning

confidence: 99%

Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland

Trier

Cowley

Waldeland

2018

Archaeological Prospection

103

104

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This article presents results of a case study within a project that seeks to develop heavily automated analysis of digital topographic data to extract archaeological information and to expedite large area mapping. Drawing on developments in computer vision and machine learning, this has the potential to fundamentally recast the capacity of archaeological prospection to cover large areas and deal with mass data, breaking a dependency on human resource. Without such developments, the potential of the vast amount of archaeological information embedded in large topographic and image-based datasets cannot be realized. The purpose of the case study reported on here is to assess existing developments in a Norwegian study against digital topographic data for the island of Arran, Scotland, examining the transferability of the approach and providing a proof of concept in a Scottish context. For Arran, three monument classes were assessedprehistoric roundhouses, shieling huts of medieval or post-medieval date, and small clearance cairns. These present different challenges to detection, with preliminary results ranging from a manageable mix of false positives and true identifications to the chaotic. The influence of variable morphology and the occurrence of other, largely natural, objects of confusion in the landscape is discussed, highlighting the potential improvements in automated detection routines offered by adding anthropogenic and natural false positives to additional confusion classes.

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Section: Methodsmentioning

confidence: 99%

Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland

Trier

Cowley

Waldeland

2018

Archaeological Prospection

103

104

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“…The result was a 1‐m resolution DTM that was converted into shaded relief images (‘hillshades’) (Štular, Kokalj, Oštir, & Nuninger, ) by applying the conventional parameters of 315° for azimuth and 45° of elevation for an artificial lighting source. However, interpreting certain kinds of terrain might also require the application of additional algorithms (Kokalj et al, ). Laplacian of Gaussian (Zhan, Liang, Cai, & Xiao, ) and Local Relief Model (Hesse, ) algorithms have been the most efficacious in revealing prehistoric features in our case.…”

Section: Methodsmentioning

confidence: 99%

Counting with the invisible record? The role of LiDAR in the interpretation of megalithic landscapes in south‐western Iberia (Extremadura, Alentejo and Beira Baixa)

Cerrillo‐Cuenca¹,

Bueno‐Ramírez

2019

Archaeological Prospection

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The megalithic sites from southwest Iberia represent one of the largest clusters of prehistoric monuments in Europe from the Neolithic and Copper Age (fifth and third millennia cal BC). Unlike other regions from western Europe, there has not been a recent effort to map the distribution of these kinds of burials across this vast territory. Therefore, this article aims to collect geographic information from three regions of southwest Iberia (Alentejo and Beira Baixa from Portugal and Extremadura from Spain) and to compare the archaeological evidence between different landscape units. We have mapped already known megaliths (ca 2000) and settlements (ca 1500) in this area. Moreover, through the interpretation of light detection and ranging (LiDAR) datasets, we have identified new walled enclosures and megaliths in the Extremadura region (Spain), the only one of the three where LiDAR data are available.The new data reveals new connections between settlements, burials and other archaeological evidence. Finally, we discuss the impact that these new data have on a new overall interpretation of megalithic landscapes from the Iberian Peninsula, stressing also the potential risks that the massive application of remote sensing can have in the production of archaeological knowledge.

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“…Numerous raster data visualization techniques exist for the illumination of small-scale positive and negative relief features [56], including hillshade [57], sky-view factor [58], openness [59], local dominance [60], and local relief modeling (LRM). Each technique possesses strengths and weaknesses when applied to the task of remote cave detection.…”

Section: Methodsmentioning

confidence: 99%

“…After experimentation with multiple techniques, the project selected LRM for its ability to highlight both positive and negative anomalies in relation to the generalized landscape [56]. The method, when properly utilized, extracts human modifications and potential cave features on the immediate level and provides effective understanding of the associations between prehistoric constructions and ritual cave spaces.…”

Section: Methodsmentioning

confidence: 99%

“…Raster creation and editing was performed within ArcMap version 10.3, while LP360 was used visualize point clouds in profile and 3D views [29] An initial local relief model was created within ArcMap using the LRM Toolbox [63]; the LRM Toolbox ran the lidar-derived DEM through eight processing steps designed to extract minor variations in elevation based on overall trends within the larger surrounding landscape (Figure 8). A circular neighborhood radius of 25 m was established for the kernel standard size based on the presumed maximum extent of possible sinkhole openings within the Vaca Plateau [48]; this kernel size has been utilized in previous landscape modeling research on similar gradually sloping terrain with positive results [56,62]. Once initial processing was complete, the 222 km 2 Las Cuevas LRM was draped over a slope model produced from the original lidar-derived DEM and divided into 500 m × 500 m tiles to correspond with the LAS point cloud grid size previously established by NCALM.…”

Section: Methodsmentioning

confidence: 99%

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Locating Cave Entrances Using Lidar-Derived Local Relief Modeling

Moyes

Montgomery²

2019

Geosciences

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Lidar (Light detection and ranging) scanning has revolutionized our ability to locate geographic features on the earth’s surface, but there have been few studies that have addressed discovering caves using this technology. Almost all attempts to find caves using lidar imagery have focused on locating sinkholes that lead to underground cave systems. As archaeologists, our work in the Chiquibul Forest Reserve, a heavily forested area in western Belize, focuses on locating potential caves for investigation. Caves are an important part of Maya cultural heritage utilized by the ancient Maya people as ritual spaces. These sites contain large numbers of artifacts, architecture, and human remains, but are being looted at a rapid rate; therefore, our goal is to locate and investigate as many sites as possible during our field seasons. While some caves are entered via sinkholes, most are accessed via vertical cliff faces or are entered by dropping into small shafts. Using lidar-derived data, our goal was to locate and investigate not only sinkholes but other types of cave entrances using point cloud modeling. In this article, we describe our method for locating potential cave openings using local relief models that require only a working knowledge of relief visualization techniques. By using two pedestrian survey techniques, we confirmed a high rate of accuracy in locating cave entrances that varied in both size and morphology. Although 100% pedestrian survey coverage delivered the highest rate accuracy in cave detection, lidar image analyses proved to be expedient for meeting project goals when considering time and resource constraints.

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Airborne laser scanning raster data visualization

Cited by 121 publications

References 0 publications

Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland

Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland

Counting with the invisible record? The role of LiDAR in the interpretation of megalithic landscapes in south‐western Iberia (Extremadura, Alentejo and Beira Baixa)

Locating Cave Entrances Using Lidar-Derived Local Relief Modeling

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