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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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 |
work_keys_str_mv |
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