基于机器学习的福建泉州世界文化遗产景观感知研究  

Research on Landscape Perception of World Cultural Heritage in Quanzhou,Fujian Based on Machine Learning

作　　者：任维 李巧婷 詹开元 傅伟聪 REN Wei;LI Qiaoting;ZHAN Kaiyuan;FU Weicong(Department of Landscape Architecture,College of Landscape Architecture and Art,Fujian Agriculture and Forestry University;Research Center for Cross-Strait Beautiful Rural Human Settlements;College of Landscape Architecture and Art,Fujian Agriculture and Forestry University)

机构地区：[1]福建农林大学风景园林与艺术学院风景园林系 [2]海峡美丽乡村人居环境研究中心 [3]福建农林大学风景园林与艺术学院

出　　处：《风景园林》2024年第11期120-129,共10页Landscape Architecture

基　　金：教育部人文社会科学研究青年基金项目“新数据环境下浙闽地区山水人居环境地域景观保护与发展研究”(编号20YJC760079);2021年福建省本科高校教育教学改革研究“新工科背景下基于‘OBE+DoPBL+大数据’的风景园林一流本科专业人才培养研究与实践”(编号FBJG20210091);福建农林大学科技创新专项基金“基于多源数据的泉州世界文化遗产景观感知研究”(编号CXZX2023005);福建农林大学科技创新专项基金“基于多源数据的闽江干流下游历史文化村镇景观评价研究”(编号CXZX2022057);福建农林大学2023年专业学位研究生课程案例库“数智化风景园林规划与设计案例库”(编号712018AL2306)。

摘　　要：【目的】既有的遗产景观感知研究多存在数据类型单一且融合度不足、机器学习等新技术方法应用尚不充分等问题,制约了遗产景观感知研究的维度与深度,因此亟待探索多模态数据有效融合的新方法及多种机器学习模型集成的新技术。【方法】以福建泉州世界文化遗产的12处遗产点为对象,基于100292份有效网络图文数据,通过系统集成潜在狄利克雷分配(latent Dirichlet allocation,LDA)主题聚类模型、多模态统一(one-for-all,OFA)图像描述模型和长短期记忆网络(long short-term memory,LSTM)情感分析模型等机器学习技术方法,从遗产点热度时空演变、遗产景观感知维度、遗产景观感知网络、遗产景观感知情感倾向4个方面进行景观感知研究。【结果】1)在遗产点热度时空演变上,遗产点热度与游客景观感知度受政策与事件驱动呈协同快速增长趋势,但存在显著的时空差异性,梯度由“高—低”两阶向“高—中—低”三阶过渡。2)在遗产景观感知维度与遗产景观感知网络上,多元融合是景观感知的文化内核,并衍生出层次丰富、以文化价值为主导的景观感知体系。遗产景观感知维度有三大类、七小类,涵盖共性及差异化感知内容;整体上,12处遗产点主题数量占比为文化价值>风景游赏>特色体验>物质载体;各遗产点差异显著,形成由各感知维度主导的4类群组。遗产景观感知高频词分布在物质载体、风景游赏、文化价值3个维度;语义网络呈“中心区域—边缘区域”结构,且中心均质、边缘松散;4个语义网络集群与LDA主题聚类感知维度匹配度高。3)在遗产景观感知情感倾向上,游客有效感知到了泉州世界文化遗产景观及其深厚的历史文化底蕴与遗产属性。遗产景观感知情感倾向整体为中性偏积极,且文本情感倾向比图像描述文本更积极;各遗产点情感指数差异大,可达性、聚集度是根�[Objective]Digital technologies have opened new avenues for quantitative research on heritage landscapes.Web image-text data,primarily driven by user-generated contents,are frequently utilized in the research on heritage landscape perception.However,existing research often grapples with limitations related to single data type and inadequate integration,alongside insufficient application of advanced technologies and methods like machine learning.There is an urgent need to explore novel methods for effective fusion of multimodal data as well as innovative techniques for integrating multivariate machine learning models.[Methods]This research reviews 12 world cultural heritage sites in Quanzhou.The Octopus Collector is applied to gather comments,images,and other web image-text data.After data cleansing and pre-processing,a total of 100,292 valid entries were obtained.Based on this dataset,the following analyses are completed.1)Popularity analysis.Based on the number of annual comments,streamgraph,a pyecharts tool,is adopted for visualized analysis of the temporal and spatial evolution of heritage site popularity.2)Image perception analysis.Latent Dirichlet allocation(LDA)topic clustering model is adopted for mining unsupervised clustering topics from of all comment texts to explore landscape perception dimensions associated with world cultural heritage in Quanzhou;one-for-all(OFA)image description model is adopted for natural language translation and description of all collected images while analyzing the landscape perception network through word frequency analysis and semantic network.3)Sentiment perception analysis.Based on all comment texts and image description texts,long short-term memory(LSTM)sentiment analysis model is adopted to analyze the sentiment tendency of overall landscape perception and landscape perception of each heritage site.[Results]1)In terms of the spatial and temporal evolution of heritage sites’popularity and tourists’landscape perceptions,despite significant spatial and temporal variabil

关 键 词：景观感知 世界遗产 遗产景观 机器学习 网络图文数据 泉州 

分 类 号：TU984[建筑科学—城市规划与设计]