Archaeological site segmentation of ancient city walls based on deep learning and LiDAR remote sensing.
• Extract ancient city wall sites at pixel level from LiDAR remote sensing data based on deep learning. • Improvement strategies in the dataset generation, model training, and post-processing steps. • A significant source of inspiration for the inspection, conservation, and monitoring of cultural he...
| Publicado en: | Journal of Cultural Heritage Vol. 66; pp. 117 - 132 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Mar2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=176226479&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 176226479 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 12962074 KK8 jtl: Journal of Cultural Heritage issn: 12962074 maglogo: N pubinfo: dt: Mar2024 vid: 66 pid: 467 pub: Elsevier B.V. artinfo: ui: 176226479 10.1016/j.culher.2023.11.005 ppf: 117 ppct: 15 formats: tig: atl: Archaeological site segmentation of ancient city walls based on deep learning and LiDAR remote sensing. aug: au: Wang, Shunli Hu, Qingwu Wang, Shaohua Ai, Mingyao Zhao, Pengcheng affil: School of Remote Sensing and Information Engineering, Hubei Luojia Laboratory, Wuhan University, Wuhan, 430079, China su: Ancient cities & towns Point cloud Deep learning Remote sensing Optical remote sensing Archaeological excavations Distance education sug: subj: Ancient cities & towns Point cloud Deep learning Remote sensing Optical remote sensing Archaeological excavations Distance education keyword: Archaeological remote sensing Archaeological site segmentation Archaeological survey LiDAR remote sensing Semantic segmentation ab: • Extract ancient city wall sites at pixel level from LiDAR remote sensing data based on deep learning. • Improvement strategies in the dataset generation, model training, and post-processing steps. • A significant source of inspiration for the inspection, conservation, and monitoring of cultural heritage. Ancient city walls, one of the most notable distinguishing features of Chinese ancient cities, are military defenses constructed of rammed earth. The ancient city walls have considerable study value because they served as the city's boundary and a symbol of power. However, as a result of natural erosion and human activities, many sites have been ruined. Existing optical remote sensing technologies, LiDAR point cloud processing algorithms, and deep learning methods are inadequate for the extraction and segmentation of ancient city wall sites. The novel semantic segmentation method for ancient city wall sites is described in this paper that extracts sites at the pixel level from LiDAR remote sensing data based on deep learning. To begin, the point cloud data collected by airborne laser scanning is processed into DEM data, and the distribution of ancient city walls in the study area is obtained through archaeological survey and expert interpretation. The dataset for deep learning semantic segmentation is then generated using image cropping and data augmentation techniques. Third, implement a U-Net semantic segmentation framework for microtopographic archaeological sites, and predict ancient city wall sites in the testing region after model training. Finally, the deep learning results are optimized using the connected component analysis method, and prediction mistakes such as holes and noise are removed. Taking Jinancheng, the capital city of the Chu kingdom, as an example, the proposed method process can identify and extract the ancient city wall sites at the pixel level, where the evaluation metrics reach 94.12% (Precision) and 81.38% (IoU). The experiment results are excellent due to improvement strategies in the dataset generation, model training, and post-processing steps. Thus, this study is significant for the current survey and protection of ancient city wall sites. The source code will be freely available at https://github.com/wshunli/Open-CHAI-CityWalls. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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