Learning and Recognizing Archeological Features from LiDAR Data

Albrecht, Conrad M; Fisher, Chris; Freitag, Marcus; Hamann, Hendrik F.; Pankanti, Sharathchandra; Pezzutti, Florencia; Rossi, Francesca

doi:10.1109/bigdata47090.2019.9005548

Cited by 25 publications

(13 citation statements)

References 19 publications

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“…An emerging trend is the shift from rule-based object detection to machine learning methods [58]. Regardless of the tools used, the method aims to complement interpretative mapping, especially in situations where so much data is available that it becomes difficult to map everything manually [59][60][61].…”

Section: Archaeological Interpretation (31-35)mentioning

confidence: 99%

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 Its novelty and significance lies in the fact that the existing comprehensive studies are outdated and the newer ones focus on selected aspects of the workflow.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: Archaeological Interpretation (31-35)mentioning

confidence: 99%

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Lozić

Štular

2021

Geosciences

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“…Different researchers have already focused on archaeological evidence detection at a raster/2D level (Figure 1), adopting machine learning (Freeland et al, 2016;Guyot et al, 2018;Niculiță, 2020;Rom et al, 2020) and deep learning (Albrecht et al, 2019;Trier et al, 2021) strategies, whereas only a few studies aimed at automatic filtering/segmenting point clouds (Hu and Yuan, 2016;Geveart et al, 2018;Bulatov et al, 2021). Generalisation and transferability capabilities are also evaluated.…”

Section: Related Workmentioning

confidence: 99%

Seeing Among Foliage With Lidar and Machine Learning: Towards a Transferable Archaeological Pipeline

Mazzacca

Grilli

Cirigliano

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Airborne LiDAR technology has become an essential tool in archaeology during the last two decades since it allows archaeologists to measure and map items or structures that would otherwise be hidden under vegetation. In order to detect and characterise the archaeological evidence, it is a common practice to extract accurate digital terrain models (DTM) by filtering out the vegetation from Airborne Laser Scanning (ALS) datasets. Although previous approaches have performed well in ALS filtration, they are still subject to several variables (flight height, forest cover, type of sensors utilised, etc.) and are frequently integrated into expensive commercial software or customised for specific locations. This study presents a workflow for treating ALS archaeological datasets using machine learning algorithms for both filtering the vegetation and detecting hidden structures. The workflow is applied to two different archaeological environments (in terms of structures, vegetation, landscape, point density), and results demonstrate that the pipeline is rapid and accurate, and the prediction model is transferable.

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“…e.g. [45]; alternatively, fusion with existing elevation models is an option Given the Land Cover ground truth labels (cf. Sec.…”

Section: Validation Of Automated Label Generationmentioning

confidence: 99%

AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning

Albrecht

Marianno²,

Klein³

2021

2021 IEEE International Conference on Big Data (Big Data)

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A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is based on rasterized statistical features extracted from surveys such as e.g. LiDAR measurements. Using simple combinations of the rasterized statistical layers, it is demonstrated that multiple classes can be generated at accuracies of ∼ 0.9. As proof of concept, we utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas with multiple land cover classes. The general method proposed here is platform independent, and it can be adapted to generate labels for other satellite modalities in order to enable machine learning on overhead imagery for land use classification and object detection.

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Learning and Recognizing Archeological Features from LiDAR Data

Cited by 25 publications

References 19 publications

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Seeing Among Foliage With Lidar and Machine Learning: Towards a Transferable Archaeological Pipeline

AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning

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