Comparison of Filters for Archaeology-Specific Ground Extraction from Airborne LiDAR Point Clouds

Štular, Benjamin; Lozić, Edisa

doi:10.3390/rs12183025

Cited by 30 publications

(71 citation statements)

References 46 publications

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“…A note on terminology is appropriate at this point. As noted elsewhere [11], we use the term algorithm to refer to theory (e.g., academic articles) and filter to implementation (e.g., in software). The term filtering is used to describe the process of applying the filter to the data.…”

Section: Point Cloud Processing and Derivation Of The Products (21-25)mentioning

confidence: 99%

“…These features are defined by point cloud processing. Arguably, the most important step is the automatic ground point classification, where each point is classified either as ground (terrain) or non-ground (off-terrain) [11,15]. This is a probabilistic rather than a deterministic process, and any classification includes false positives (ground points classified as non-ground) and false negatives (non-ground points classified as ground) [33].…”

Section: Point Cloud Processing and Derivation Of The Products (21-25)mentioning

confidence: 99%

“…More importantly, mapping of features makes it possible to develop a more profound understanding of the archaeological landscapes [10]. In comparison to other fields, the use of airborne LiDAR-derived data in archaeology is specific in several ways [11]. Therefore, an archaeology-specific airborne LiDAR data processing workflow from mission planning to archiving has been developed.…”

Section: Introductionmentioning

confidence: 99%

“…However, this process is rarely documented and there are no accepted schemas, let alone standards, to do so. As a result, there is a risk that airborne LiDAR data processing from data acquisition to product derivation is accepted as a black box process and its results as "hard data" [11][12][13][14][15][16]. Unfortunately, this has become the norm rather than the exception.…”

Section: Introductionmentioning

confidence: 99%

“…Despite terminological differences, the studies cited agree on the general workflow. However, since both comprehensive overviews [16,27] were written, several new steps or exclusive aspects have been introduced in the studies that focused on raw data [29], point cloud processing [11,15], or on archaeological interpretation [13,21]. As a result, there is currently no agreement on the specific steps or terminology for airborne LiDAR data processing from data acquisition to data archiving in archaeology.…”

Section: Introductionmentioning

confidence: 99%

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Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Lozić

Štular

2021

Geosciences

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Airborne LiDAR is a widely accepted tool for archaeological prospection. Over the last decade an archaeology-specific data processing workflow has been evolving, ranging from raw data acquisition and processing, point cloud processing and product derivation to archaeological interpretation, dissemination and archiving. Currently, though, there is no agreement on the specific steps or terminology. This workflow is an interpretative knowledge production process that must be documented as such to ensure the intellectual transparency and accountability required for evidence-based archaeological interpretation. However, this is rarely the case, and there are no accepted schemas, let alone standards, to do so. As a result, there is a risk that the data processing steps of the workflow will be accepted as a black box process and its results as “hard data”. The first step in documenting a scientific process is to define it. Therefore, this paper provides a critical review of existing archaeology-specific workflows for airborne LiDAR-derived topographic data processing, resulting in an 18-step workflow with consistent terminology. Its novelty and significance lies in the fact that the existing comprehensive studies are outdated and the newer ones focus on selected aspects of the workflow. Based on the updated workflow, a good practice example for its documentation is presented.

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Section: Point Cloud Processing and Derivation Of The Products (21-25)mentioning

confidence: 99%

Section: Point Cloud Processing and Derivation Of The Products (21-25)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Lozić

Štular

2021

Geosciences

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Human‐in‐the‐loop development of spatially adaptive ground point filtering pipelines—An archaeological case study

Doneus

Höfle

Kempf

et al. 2022

Archaeological Prospection

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LiDAR data have become indispensable for research in archaeology and a variety of other topographic applications. To derive products (e.g. digital terrain or feature models, individual trees, buildings), the 3D LiDAR points representing the desired objects of interest within the acquired and georeferenced point cloud need to be identified. This process is known as classification, where each individual point is assigned to an object class. In archaeological prospection, classification focuses on identifying the object class 'ground points'. These are used to interpolate digital terrain models exposing the microtopography of a terrain to be able to identify and map archaeological and palaeoenvironmental features. Setting up such classification workflows can be time-consuming and prone to information loss, especially in geographically heterogeneous landscapes. In such landscapes, one classification setting can lead to qualitatively very different results, depending on varying terrain parameters such as steepness or vegetation density. In this paper, we are focussing on a special workflow for optimal classification results in these heterogeneous environments, which integrates expert knowledge. We present a novel Pythonbased open-source software solution, which helps to optimize this process and creates a single digital terrain model by an adaptive classification based on spatial segments. The advantage of this approach for archaeology is to produce coherent digital terrain models even in geomorphologically heterogenous areas or areas with patchy vegetation. The software is also useful to study the effects of different algorithm and parameter combinations on digital terrain modelling with a focus on a practical and time-saving implementation. As the developed pipelines and all meta-information are made available with the resulting data set, classification is white boxed and consequently scientifically comprehensible and repeatable.Together with the software's ability to simplify classification workflows significantly, it will be of interest for many applications also beyond the examples shown from archaeology.

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Intensively Cultivated Roman Villae Estates: Case Study of Medulin Bay (Istria, Croatia)

Doneus,

Doneus

2024

Archaeological Prospection

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Our image of the Roman landscape of Istria is characterised by large‐scale centuriation and architectural remains of Roman villae. Detection and mapping of other, less dramatic landscape features require systematic large‐scale prospection, but this faces significant difficulties in the Mediterranean environment. However, the developments in the field of airborne laser scanning offer the possibility to create archaeologically usable digital terrain models under water and under very dense and low maquis vegetation. This paper reports on the use of terrain models created using a green laser and a sophisticated archaeologically driven ground point filtering strategy. Combined with archaeological aerial photo interpretation, this provides the means for landscape mapping and interpretation that has revealed a wealth of archaeological structures hinting at Roman agricultural practices and landscape. Our case study is based on a laser scan of about 24 km2 of land and underwater terrain in Medulin Bay. Processed, visualised and interpreted for archaeological purposes, the data reveal not only features ranging from prehistoric hilltop settlements to modern military installations but also features a complex picture of the Roman land use. Of particular interest is the large number of planting pits, which extend over a total length of 4 km. They were laid on a regular grid of approximately 35 × 35 m, sometimes combined in contiguous parcels. They can be interpreted as remains of orchards or tree nurseries of Roman date, and the paper examines also the question of whether they can be linked to the associated Roman estates. The case study area presented here shows that the potential of remote sensing methods goes far beyond the mere finding of traditional sites but can open up new landscape‐scale perspectives on regions that have been archaeologically little explored.

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Comparison of Filters for Archaeology-Specific Ground Extraction from Airborne LiDAR Point Clouds

Cited by 30 publications

References 46 publications

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Human‐in‐the‐loop development of spatially adaptive ground point filtering pipelines—An archaeological case study

Intensively Cultivated Roman Villae Estates: Case Study of Medulin Bay (Istria, Croatia)

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