Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology
Archaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Dig...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/11/2535 |
| _version_ | 1797491885529366528 |
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| author | M. Fabian Meyer-Heß Ingo Pfeffer Carsten Juergens |
| author_facet | M. Fabian Meyer-Heß Ingo Pfeffer Carsten Juergens |
| author_sort | M. Fabian Meyer-Heß |
| collection | DOAJ |
| description | Archaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Digital Terrain Models, special visualizations, and automated site detection. The latter can generate comprehensive datasets with manageable effort that are useful for answering large-scale archaeological research questions. This article presents a highly automated workflow, in which a Convolutional Neural Network is used to detect burial mounds in the proximity of remotely located hollow ways. Detected mounds are then analyzed with respect to their distribution and a possible spatial relation to hollow ways. The detection works well, produces a reasonable number of results, and achieved a precision of at least 77%. The distribution of mounds shows a clear maximum in the radius of 2000–2500 m. This supports future research such as visibility or cost path analysis. |
| first_indexed | 2024-03-10T00:55:40Z |
| format | Article |
| id | doaj.art-4fd038af6d944d07bfebc46dfdf6910a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:55:40Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4fd038af6d944d07bfebc46dfdf6910a2023-11-23T14:43:19ZengMDPI AGRemote Sensing2072-42922022-05-011411253510.3390/rs14112535Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in ArchaeologyM. Fabian Meyer-Heß0Ingo Pfeffer1Carsten Juergens2Geomatics Group, Geography Department, Ruhr University Bochum, 44801 Bochum, GermanyLWL-Archaeology for Westphalia, 48157 Münster, GermanyGeomatics Group, Geography Department, Ruhr University Bochum, 44801 Bochum, GermanyArchaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Digital Terrain Models, special visualizations, and automated site detection. The latter can generate comprehensive datasets with manageable effort that are useful for answering large-scale archaeological research questions. This article presents a highly automated workflow, in which a Convolutional Neural Network is used to detect burial mounds in the proximity of remotely located hollow ways. Detected mounds are then analyzed with respect to their distribution and a possible spatial relation to hollow ways. The detection works well, produces a reasonable number of results, and achieved a precision of at least 77%. The distribution of mounds shows a clear maximum in the radius of 2000–2500 m. This supports future research such as visibility or cost path analysis.https://www.mdpi.com/2072-4292/14/11/2535landscape archaeologyLiDARautomated detectionCNNburial moundhollow way |
| spellingShingle | M. Fabian Meyer-Heß Ingo Pfeffer Carsten Juergens Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology Remote Sensing landscape archaeology LiDAR automated detection CNN burial mound hollow way |
| title | Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology |
| title_full | Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology |
| title_fullStr | Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology |
| title_full_unstemmed | Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology |
| title_short | Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology |
| title_sort | application of convolutional neural networks on digital terrain models for analyzing spatial relations in archaeology |
| topic | landscape archaeology LiDAR automated detection CNN burial mound hollow way |
| url | https://www.mdpi.com/2072-4292/14/11/2535 |
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