Automated Detection of Field Monuments in Digital Terrain Models of Westphalia Using OBIA

While Light Detection and Ranging (LiDAR) revolutionized archaeological prospection and different visualizations were developed, an automated detection of cultural heritage still poses a significant challenge. Therefore, geographers and archaeologists from Westphalia, Germany are developing automate...

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Main Authors: M. Fabian Meyer, Ingo Pfeffer, Carsten Jürgens
Format: Article
Language:English
Published: MDPI AG 2019-02-01
Series:Geosciences
Subjects:
Online Access:https://www.mdpi.com/2076-3263/9/3/109
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spelling doaj-c96bfc245a8542a3991affb16bae31b82020-11-25T01:29:15ZengMDPI AGGeosciences2076-32632019-02-019310910.3390/geosciences9030109geosciences9030109Automated Detection of Field Monuments in Digital Terrain Models of Westphalia Using OBIAM. Fabian Meyer0Ingo Pfeffer1Carsten Jürgens2Department of Geography, Ruhr-University Bochum, Workgroup of Geomatics, 44801 Bochum, GermanyLWL-Archaeology for Westphalia, 48157 Münster, GermanyDepartment of Geography, Ruhr-University Bochum, Workgroup of Geomatics, 44801 Bochum, GermanyWhile Light Detection and Ranging (LiDAR) revolutionized archaeological prospection and different visualizations were developed, an automated detection of cultural heritage still poses a significant challenge. Therefore, geographers and archaeologists from Westphalia, Germany are developing automated workflows for classifying field monuments from special terrain models. For this project, a combination of GIS, Python, and Object-Based Image Analysis (OBIA) is used. It focuses on three common types of monuments: Ridge and Furrow areas, Burial Mounds, and Motte-and-Bailey castles. The latter two are not classified binary, but in multiple classes, depending on their degree of erosion. This simplifies interpretation by highlighting the most interesting structures without losing the others. The results confirm that OBIA is suitable for detecting field monuments with hit rates of ~90%. A drawback is its dependency on the use of special terrain models like the Difference Map. Further limitations arise in complex terrain situations.https://www.mdpi.com/2076-3263/9/3/109automated detectionOBIALiDARDifference Mapfield monumentBurial MoundMotte-and-Bailey castleRidge and Furrow
collection DOAJ
language English
format Article
sources DOAJ
author M. Fabian Meyer
Ingo Pfeffer
Carsten Jürgens
spellingShingle M. Fabian Meyer
Ingo Pfeffer
Carsten Jürgens
Automated Detection of Field Monuments in Digital Terrain Models of Westphalia Using OBIA
Geosciences
automated detection
OBIA
LiDAR
Difference Map
field monument
Burial Mound
Motte-and-Bailey castle
Ridge and Furrow
author_facet M. Fabian Meyer
Ingo Pfeffer
Carsten Jürgens
author_sort M. Fabian Meyer
title Automated Detection of Field Monuments in Digital Terrain Models of Westphalia Using OBIA
title_short Automated Detection of Field Monuments in Digital Terrain Models of Westphalia Using OBIA
title_full Automated Detection of Field Monuments in Digital Terrain Models of Westphalia Using OBIA
title_fullStr Automated Detection of Field Monuments in Digital Terrain Models of Westphalia Using OBIA
title_full_unstemmed Automated Detection of Field Monuments in Digital Terrain Models of Westphalia Using OBIA
title_sort automated detection of field monuments in digital terrain models of westphalia using obia
publisher MDPI AG
series Geosciences
issn 2076-3263
publishDate 2019-02-01
description While Light Detection and Ranging (LiDAR) revolutionized archaeological prospection and different visualizations were developed, an automated detection of cultural heritage still poses a significant challenge. Therefore, geographers and archaeologists from Westphalia, Germany are developing automated workflows for classifying field monuments from special terrain models. For this project, a combination of GIS, Python, and Object-Based Image Analysis (OBIA) is used. It focuses on three common types of monuments: Ridge and Furrow areas, Burial Mounds, and Motte-and-Bailey castles. The latter two are not classified binary, but in multiple classes, depending on their degree of erosion. This simplifies interpretation by highlighting the most interesting structures without losing the others. The results confirm that OBIA is suitable for detecting field monuments with hit rates of ~90%. A drawback is its dependency on the use of special terrain models like the Difference Map. Further limitations arise in complex terrain situations.
topic automated detection
OBIA
LiDAR
Difference Map
field monument
Burial Mound
Motte-and-Bailey castle
Ridge and Furrow
url https://www.mdpi.com/2076-3263/9/3/109
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