摘要
【目的】国家文化公园建设背景下,长城、大运河等线性文化遗产本体上的旅游与游憩活动正在迅速拓展至沿线的带状区域,国家文化公园的景观规划设计就需要从资源价值角度回答“在哪儿能看见景观”的问题,其关键在于有效解决大规模文化遗产视觉感知研究中存在的景观多维语义表达不充分和可视计算量大的问题。【方法】本文基于格式塔理论,综合大型线性文化遗产景观蕴含的系统层级、空间结构、历史功能及形态美学等多维语义,设计了景观语义特征点自动提取与可视区位计算方法。首先,遵循突出线性文化遗产景观意义和视觉价值的原则,将遗产景观抽象概括为一组包含景观语义信息的特征点;其次,基于景观语义特征点计算可视区位,并选用NetCDF多维数据格式将可视计算结果与景观语义特征点集成组织存储,以特征点为纽带将景观多维语义与可视区位一体化表达;进而,通过可视区位挖掘方法,实现对于景观语义的“概括—表征—还原”过程;【结果】选取京津冀行政区范围内的明代长城遗产进行实证研究,基于DEM和文物普查等基础数据共提取出53454个特征点,并进行了可视区位计算与组织,实地验证可视区位的特征点数量和内容吻合度平均值分别为76.37%和70.69%,表明方法能够快速提取表征大规模遗产景观语义的特征点,可视区位计算结果具有较高可信度,基于特征点的可视区位信息挖掘能够寻找特定区位上的可视景观语义和感知特定景观语义的优质可视区位,实现景观语义和可视区位的双向查询。【结论】多维语义特征点提取与景观可视区位计算方法可以为大尺度文化遗产可视分析研究和景观视觉价值挖掘提供方法和思路。
[Objectives] Under the background of National Cultural Park construction, tourism and recreational activities related to lineal cultural heritage sites, such as the Great Wall and the Grand Canal, are rapidly expanding along their routes. The landscape planning and design of these parks must address the question: "where can the landscape be seen?", a query rooted in the evaluation of resource value. The challenge involves two core issues in large-scale visual perception research of lineal cultural heritage: insufficient multi-dimensional semantic representation of landscapes, and computationally intensive visible location analysis. [Methods] Grounded in Gestalt theory, this study considers the multi-dimensional semantics embedded in the landscapes of large-scale lineal cultural heritage, including hierarchy systems, spatial structure, historical function, and morphological aesthetics. A method is proposed for the automatic extraction of landscape semantic feature points and the computation of visible locations. First, adhering to the principle of emphasizing the significance and visual value of heritage landscapes, these landscapes are abstracted and summarized into a set of feature points containing semantic information. Second, visible locations are computed based on these points, with the NetCDF multi-dimensional data format employed to integrate, organize, and store both the visibility computation results and semantic feature points. By using these feature points as a bridge, an integrated representation of multi-dimensional landscape semantics and their corresponding visible locations is achieved. Furthermore, through visible location mining, the process of "summarization—representation—restoration" of the landscape semantics is realized. [Results] The empirical study focuses on the Ming Great Wall in the Beijing-Tianjin-Hebei region. Based on datasets such as DEM and cultural relics surveys, a total of 53,454 feature points were extracted, followed by visibility computation and data organization. Field verification shows that the average coincidence rates for the number and content of feature points in visible locations are 76.37% and 70.69%, respectively. These results demonstrate that the proposed method can efficiently extract feature points that represent the semantics of large-scale heritage landscapes, and that the computed visible locations exhibit high reliability. Visible location mining enables the identification of landscape semantics at specific viewpoints, as well as the discovery of high-quality viewpoints for perceiving particular semantics, thus enabling two-way querying between semantic content and visible locations. [Conclusions] The proposed method for multi-dimensional semantic feature point extraction and landscape visible location computation offers new approaches and perspectives for the visual analysis of large-scale cultural heritage and the exploration of landscape visual value.
作者
李照航
邢倩
郭风华
李仁杰
LI Zhaohang;XING Qian;GUO Fenghua;LI Renjie(College of Geographical Sciences,Hebei Normal University,Shijiazhuang 050024,China;Institute of Geographical Sciences,Hebei Academy of Sciences/Hebei Technology Innovation Center for Geographic Information Application,Shijiazhuang 050011,China;GeoComputation and Planning Center of Hebei Normal University,Shijiazhuang 050024,China;Hebei Provincial Key Laboratory of Philosophy and Social Sciences"Research Laboratory of Geographic Big Data Computing and Resource Planning",Shijiazhuang 050024,China;Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change,Shijiazhuang 050024,China)
出处
《地球信息科学学报》
北大核心
2025年第7期1721-1737,共17页
Journal of Geo-information Science
基金
河北省自然科学基金项目(D2023205011)
国家自然科学基金项目(41471127)。
关键词
线性文化遗产
景观语义特征点
景观可视区位
多维语义
京津冀明长城
lineal cultural heritage
landscape semantic feature point
landscape visible location
multi-dimensional semantic
Great Wall of Ming dynasty in Beijing-Tianjin-Hebei
