Archaeological site segmentation of ancient city walls based on deep learning and LiDAR remote sensing
发布时间：2025-01-07 点击次数：
DOI码：10.1016/j.culher.2023.11.005
所属单位：School of Remote Sensing and Information Engineering, Wuhan University, China
发表刊物：Journal of Cultural Heritage
关键字：Archaeological remote sensing,Archaeological site segmentation,Deep learning,LiDAR remote sensing,Semantic segmentation,Archaeological survey
摘要：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 .(c) 2023 Consiglio Nazionale delle Ricerche (CNR). Published by Elsevier Masson SAS. All rights reserved.
合写作者：Pengcheng Zhao,Mingyao Ai,Shaohua Wang,Shunli Wang,胡庆武
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：66
页面范围：117-131
ISSN号：1296-2074
是否译文：否
CN号：Scopus:2-s2.0-85178335169,WOS:001130064000001
发表时间：2024-03-01
