[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4443":3,"related-4443":98},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":97},4443,"Geospatial Education at the GIS–GeoAI Interface: A PRISMA-Guided Bibliometric Mapping of a Scopus Corpus, 2002–2025","https:\u002F\u002Fdoi.org\u002F10.24114\u002Fjg.v18i2.75596","Geospatial information is used in education as disciplinary content and as a learning technology, while computational geospatial research increasingly employs artificial intelligence, machine learning, deep learning, and remote sensing. This study maps the publication performance and thematic structure of a broad Scopus corpus at the interface of geospatial technology and education from 2002 to 2025. A PRISMA-guided review and bibliometric analysis were conducted on 125 documents from 68 sources using bibliometrix and Biblioshiny in R. The corpus showed an annual publication growth rate of 10.99%, an average of 11.93 citations per document, and 2.81 authors per document. The Journal of Geography was the most productive source, while De Lázaro-Torres and De Miguel González were the most productive authors. Co-word analysis identified an educational GIS cluster, a geospatial technology and learning cluster, and a computational geospatial cluster containing GeoAI-related terms. The increased visibility of the computational cluster in 2022–2025 indicates an emerging thematic orientation, not evidence that GeoAI has replaced GIS-based education or already dominates educational practice. Because the search expression included the broad term learning and the corpus retained several technical geospatial records, the findings should be interpreted as an exploratory map of the education–technology interface. The proposed instructional sequence is therefore a theory-informed research proposition that requires empirical validation","地理空间信息在教育中既作为学科内容，也作为学习技术使用，而计算地理空间研究日益采用人工智能、机器学习、深度学习与遥感。本研究绘制了2002年至2025年间Scopus数据库中地理空间技术与教育交叉领域广泛文献的出版表现与主题结构。采用PRISMA导向的综述与文献计量分析，使用R语言中的bibliometrix和Biblioshiny对来自68个来源的125篇文献进行分析。该文献集显示年出版增长率为10.99%，篇均被引11.93次，篇均作者2.81人。《地理学杂志》（Journal of Geography）是最高产来源，而De Lázaro-Torres和De Miguel González是最高产作者。共词分析识别出教育GIS聚类、地理空间技术与学习聚类，以及包含GeoAI相关术语的计算地理空间聚类。计算聚类在2022—2025年可见度上升，表明一种新兴的主题取向，而非GeoAI已取代基于GIS的教育或已主导教育实践的证据。由于检索式包含宽泛术语“学习”，且文献集保留了一些技术性地地理空间记录，研究结果应被解释为教育—技术界面的探索性图谱。因此，所提出的教学序列是一种有理论依据的研究命题，尚需实证验证。",null,"JURNAL GEOGRAFI","2026-10-06T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为地理空间教育与GeoAI交叉领域的文献计量论文，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"GeoAI","文献计量","地理信息系统","地理空间教育",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":66},[32,40,47,58],{"code":33,"score":34,"supported":35,"title":36,"abstract":38,"tags":39},"I7",15,true,[37],"bibliometric",[22,37],[22],{"code":41,"score":42,"supported":35,"title":43,"abstract":45,"tags":46},"I4",12,[44],"GIS",[44],[23],{"code":48,"score":49,"supported":35,"title":50,"abstract":51,"tags":57},"I2",9,[],[52,53,54,55,56],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[],{"code":59,"score":60,"supported":35,"title":61,"abstract":62,"tags":65},"I3",6,[],[63,64],"遥感","remote sensing",[],"农业相关性需要复核",[68,69],"GeoAI 地理空间教育","Scopus 文献计量 地理","GeoAI地理空间教育-4443",1,"10.24114\u002Fjg.v18i2.75596",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":15,"oa_url":88,"card":89,"direction":95,"ingested_from":96},"W7220698962",[76,79,82,85],{"name":77,"orcid":78},"Sony Nugratama Hijrawadi","https:\u002F\u002Forcid.org\u002F0000-0001-7694-3062",{"name":80,"orcid":81},"Epon Ningrum","https:\u002F\u002Forcid.org\u002F0000-0003-2952-8231",{"name":83,"orcid":84},"Nandi Nandi","https:\u002F\u002Forcid.org\u002F0000-0002-5994-1142",{"name":86,"orcid":87},"Lili Somantri","https:\u002F\u002Forcid.org\u002F0000-0002-6783-5948","https:\u002F\u002Fjurnal.unimed.ac.id\u002F2012\u002Findex.php\u002Fgeo\u002Farticle\u002Fdownload\u002F75596\u002F30929",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"用PRISMA与文献计量法梳理2002–2025年GIS与GeoAI教育交叉领域的研究格局。","PRISMA综述+文献计量，R的bibliometrix\u002FBiblioshiny","共词分析识别出教育GIS、地理空间技术与学习、计算地理空间三簇，GeoAI簇2022–2025年关注","农业人工智能与决策模型","可将GeoAI教育主题图谱延伸至农业信息化人才培养，检验所提教学序列的实证效果。","农业遥感与作物表型","openalex","2026-10-08T23:30:31.197022Z",{"total":60,"page":71,"page_size":60,"items":99},[100,166,262,317,369,440],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":114,"tags":116,"primary_topic":121,"secondary_topics":127,"classification":128,"search_phrases":143,"slug":146,"view_count":15,"doi":147,"paper":148,"created_at":165},5493,"Mapping Research on the Implementation of Smart Village Policies in Indonesia: A Bibliometric Study for the Years 2017-2025","https:\u002F\u002Fdoi.org\u002F10.66254\u002Fjgssvol5issue2page333-347","This research aims to analyze the role of public sector policies in the development of Smart Villages in Indonesia, focusing on the utilization of information and communication technology (ICT) to improve the quality of life in rural communities. Although the Indonesian government has launched various programs such as the Digital Village Program and Smart City, the implementation of these policies still faces various challenges, including limited infrastructure, low digital literacy, and lack of inter-agency coordination. This research uses a bibliometric approach to map relevant research and policy trends, as well as to identify challenges and opportunities in the development of Smart Villages. The research findings indicate that despite progress, existing public sector policies have not yet been fully effective in addressing the digital divide between villages and cities. This research recommends the need for more inclusive and integrated policy improvements, as well as strengthening human resource capacities in villages to support the creation of sustainable and competitive Smart Villages.","本研究旨在分析公共部门政策在印度尼西亚智慧村（Smart Villages）发展中的作用，重点关注利用信息通信技术（ICT）提升农村社区生活质量。尽管印度尼西亚政府已推出数字村计划（Digital Village Program）和智慧城市（Smart City）等多项举措，但这些政策的实施仍面临诸多挑战，包括基础设施有限、数字素养偏低以及跨机构协调不足。本研究采用文献计量学方法，梳理相关研究与政策趋势，并识别智慧村发展中的挑战与机遇。研究结果表明，尽管取得了一定进展，现有公共部门政策在弥合城乡数字鸿沟方面尚未充分发挥效用。本研究建议有必要进行更具包容性和整合性的政策改进，同时加强农村人力资源能力建设，以支持创建可持续且具有竞争力的智慧村。","Journal of Government Science Studies\u002FJournal Of Government Science Studies","2026-10-20T00:00:00Z",71,{"impact":110,"substance":111,"depth":34,"authority":112,"freshness":49,"relevant":71,"comment":113},16,18,13,"该文献计量研究系统梳理了印尼智慧乡村政策实施进展与数字鸿沟挑战，对数字乡村国际比较有参考价值，但属区域性研究，影响层级有限。",[115],{"name":106,"url":103},[117,118,119,22,120],"数字乡村","印尼","数字鸿沟","智慧乡村",{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":129,"locked":14,"version":28,"method":29,"evidence":130},"classified",{"candidates":131,"agricultural_evidence":35},[132,136],{"code":33,"score":34,"supported":35,"title":133,"abstract":134,"tags":135},[37],[22,37],[22],{"code":122,"score":137,"supported":35,"title":138,"abstract":139,"tags":142},11,[],[119,140,141],"digital village","digital divide",[117,119],[144,145],"印尼 智慧乡村","印尼 数字村 数字鸿沟","印尼智慧乡村-5493","10.66254\u002Fjgssvol5issue2page333-347",{"doi":147,"openalex_id":149,"authors":150,"venue":106,"cited_by_count":15,"oa_url":103,"card":159,"direction":163,"ingested_from":96},"W7223067869",[151,153,156],{"name":152,"orcid":9},"Syaifulrijal Mahulauw",{"name":154,"orcid":155},"Wahab Tuanaya","https:\u002F\u002Forcid.org\u002F0009-0001-9236-8149",{"name":157,"orcid":158},"Marno Wance","https:\u002F\u002Forcid.org\u002F0000-0001-6663-7598",{"tldr":160,"method":161,"finding":162,"direction":163,"opportunity":164},"用文献计量法梳理2017-2025年印尼智慧村政策实施研究，识别挑战与机会。","文献计量分析，覆盖2017-2025年相关文献与政策趋势。","政策尚未有效弥合城乡数字鸿沟，需更包容整合的政策与人力建设。","数字乡村与农业信息化","可延伸至跨国比较或政策效果量化评估，聚焦数字鸿沟与跨部门协同机制。","2026-10-21T23:30:33.962760Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":9,"source_name":172,"source_url":169,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":174,"score_detail":175,"sources":180,"tags":182,"primary_topic":186,"secondary_topics":190,"classification":197,"search_phrases":235,"slug":238,"view_count":15,"doi":239,"paper":240,"created_at":261},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",80,{"impact":111,"substance":176,"depth":111,"authority":177,"freshness":178,"relevant":71,"comment":179},22,14,8,"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[181],{"name":172,"url":169},[183,184,53,185,22],"智慧农业","农业人工智能","食品安全",{"code":48,"slug":187,"name":188,"path":189,"parent_code":126},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[191],{"code":192,"slug":193,"name":194,"path":195,"parent_code":196},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":129,"locked":14,"version":28,"method":29,"evidence":198},{"candidates":199,"agricultural_evidence":35},[200,207,212,216,223,230],{"code":48,"score":201,"supported":35,"title":202,"abstract":203,"tags":206},19,[56],[52,53,204,54,55,56,205],"计算机视觉","computer vision",[184,53],{"code":192,"score":34,"supported":35,"title":208,"abstract":210,"tags":211},[209],"food safety",[185,209],[185],{"code":33,"score":34,"supported":35,"title":213,"abstract":214,"tags":215},[37],[22,37],[22],{"code":217,"score":60,"supported":35,"title":218,"abstract":219,"tags":222},"H7",[],[220,221],"气候变化","climate change",[],{"code":224,"score":225,"supported":35,"title":226,"abstract":227,"tags":229},"A8",3,[],[228],"food security",[],{"code":231,"score":71,"supported":14,"title":232,"abstract":233,"tags":234},"I1",[],[],[183],[236,237],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":239,"openalex_id":241,"authors":242,"venue":172,"cited_by_count":15,"oa_url":9,"card":256,"direction":93,"ingested_from":96},"W7222920742",[243,245,247,250,253],{"name":244,"orcid":9},"Harang Lee",{"name":246,"orcid":9},"지은서",{"name":248,"orcid":249},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":251,"orcid":252},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":254,"orcid":255},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":257,"method":258,"finding":259,"direction":93,"opportunity":260},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","2026-10-20T23:30:31.567802Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":270,"sources":272,"tags":274,"primary_topic":9,"secondary_topics":277,"classification":278,"search_phrases":292,"slug":295,"view_count":15,"doi":296,"paper":297,"created_at":316},5325,"Spatio-Temporal Analysis of Shoreline Change Along the Machilipatnam Coast Using Landsat Imagery and QGIS","https:\u002F\u002Fdoi.org\u002F10.59543\u002Fqjw9sw04","Shoreline analysis is an important aspect of coastal studies, as coastlines are continuously influenced by natural processes such as waves, tides, currents, and human activities. Monitoring these changes is essential for effective coastal management, environmental protection and sustainable development. This research work focuses on the analysis of shoreline characteristics along the Machilipatnam coast in Krishna District, Andhra Pradesh, using geospatial techniques. Landsat ETM + satellite imagery for the period 2018 to 2022 was used as the primary data source. The satellite data provides multispectral information with moderate spatial resolution, making it suitable for coastal analysis. The images were processed using QGIS software, where band combination and colour composite techniques were applied to enhance the visual distinction between land and water features. A layer stack approach was used to organize raster data for better interpretation. The shoreline was identified based on textural variations and digitized as a vector line layer. Additionally, the land and water regions were digitized as polygon layers to enable spatial analysis. Various GIS tools were used to calculate shoreline length and area measurements, allowing for systematic comparison across multiple years. This study demonstrates the effectiveness of integrating remote sensing and GIS techniques for shoreline mapping and analysis.","海岸线分析是海岸研究的一个重要方面，因为海岸线持续受到波浪、潮汐、海流等自然过程以及人类活动的影响。监测这些变化对于有效的海岸管理、环境保护和可持续发展至关重要。本研究工作侧重于利用地理空间技术分析安得拉邦克里希纳区马奇利帕特南海岸的海岸线特征。研究使用2018年至2022年期间的Landsat ETM+卫星影像作为主要数据源。卫星数据提供具有中等空间分辨率的多光谱信息，适合用于海岸分析。影像使用QGIS软件进行处理，应用波段组合和彩色合成技术以增强陆地与水体的视觉区分度。采用图层堆叠方法组织栅格数据以便更好地解译。基于纹理差异识别海岸线，并将其数字化为矢量线图层。此外，将陆地区域和水域数字化为多边形图层以实现空间分析。使用多种GIS工具计算海岸线长度和面积测量值，从而能够对多个年份进行系统比较。本研究证明了集成遥感与GIS技术进行海岸线制图与分析的有效性。","Journal of Urban Intelligence and Smart Systems","2026-10-17T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":271},"印度海岸线遥感研究，与三农、农业信息化无直接关联，不建议进入每日精选。",[273],{"name":268,"url":265},[275,23,276],"遥感监测","海岸线变化",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":279},{"candidates":280,"agricultural_evidence":14,"reason":66},[281,286],{"code":59,"score":13,"supported":35,"title":282,"abstract":283,"tags":285},[],[63,64,284],"multispectral",[275],{"code":41,"score":13,"supported":35,"title":287,"abstract":288,"tags":291},[],[289,44,290],"空间分析","spatial analysis",[23],[293,294],"Machilipatnam 海岸线 Landsat","地理信息系统 海岸线变化 遥感监测","Machilipatnam海岸线Landsat-5325","10.59543\u002Fqjw9sw04",{"doi":296,"openalex_id":298,"authors":299,"venue":268,"cited_by_count":15,"oa_url":310,"card":311,"direction":95,"ingested_from":96},"W7222847874",[300,302,305,307],{"name":301,"orcid":9},"M.L. Sirisha",{"name":303,"orcid":304},"Subhashish Dey","https:\u002F\u002Forcid.org\u002F0000-0002-3408-7429",{"name":306,"orcid":9},"Seelam Srikanth",{"name":308,"orcid":309},"G.T.N. Veerendra","https:\u002F\u002Forcid.org\u002F0000-0002-2325-0243","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F331\u002F239",{"tldr":312,"method":313,"finding":314,"direction":95,"opportunity":315},"用Landsat影像和QGIS分析印度Machilipatnam海岸2018-2022年岸线时空变化","Landsat ETM+多光谱影像，QGIS波段组合、图层堆叠与矢量化岸线提取。","遥感与GIS集成可有效制图并量化岸线长度与面积年际变化。","可借鉴其岸线时序提取流程，迁移到耕地边界或灌溉水体的遥感动态监测。","2026-10-19T23:30:23.214043Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":325,"sources":327,"tags":329,"primary_topic":9,"secondary_topics":332,"classification":333,"search_phrases":348,"slug":351,"view_count":15,"doi":352,"paper":353,"created_at":368},5191,"Artificial intelligence in immovable cultural heritage research: a bibliometric and knowledge mapping review","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs43238-026-00324-1","Abstract In recent years, the application of artificial intelligence (AI) in immovable cultural heritage research has expanded rapidly, supporting new approaches to heritage documentation, condition assessment, conservation, management, and public communication. In this study, immovable cultural heritage primarily refers to historic buildings, archaeological sites, monuments, cultural landscapes, and other heritage places with fixed spatial locations. Movable cultural objects, museum collections, and intangible cultural heritage are not systematically examined unless the relevant studies directly address the documentation, monitoring, restoration, conservation, or management of immovable heritage. Despite the growing volume of research, existing studies have largely focused on individual technical applications, while comprehensive bibliometric analyses and knowledge-structure mapping from a global perspective remain limited. To address this gap, this study retrieved 382 English-language publications from the Web of Science Core Collection covering the period from 1 January 2015 to 1 October 2025. Using bibliometric methods and analytical tools including VOSviewer, CiteSpace, and ArcGIS, the study examines publication trends, geographical distribution, disciplinary categories, major authors, institutional affiliations, collaboration networks, thematic evolution, and emerging research topics. The results show that publication output increased substantially after 2020. China, Italy, and Spain were among the most productive countries and occupied important positions in the international collaboration network. Major research themes included digital heritage, structural health monitoring, virtual reconstruction, deep learning, remote sensing, and sustainability-related applications. These findings indicate that AI-related research on immovable cultural heritage is strongly represented in engineering, conservation science, computer science, remote sensing, and related technical fields. However, this pattern should not be interpreted as evidence of a general disciplinary shift across cultural heritage studies as a whole. Rather, it reflects the technological orientation of the literature captured by the present search strategy, which was limited to English-language publications indexed in the Web of Science Core Collection. This study maps the development and knowledge structure of AI applications in immovable cultural heritage research and provides quantitative evidence for identifying major research areas, collaboration patterns, methodological tendencies, and future research needs.","摘要 近年来，人工智能（AI）在不可移动文物保护研究中的应用迅速扩展，为遗产记录、状况评估、保护、管理和公众传播提供了新的方法路径。本研究中的不可移动文物主要指历史建筑、考古遗址、纪念物、文化景观及其他具有固定空间位置的遗产场所。可移动文物、博物馆藏品和非物质文化遗产不在系统考察范围内，除非相关研究直接涉及不可移动遗产的记录、监测、修复、保护或管理。尽管研究数量不断增长，现有研究大多聚焦于单项技术应用，而从全球视角开展的综合文献计量分析和知识结构图谱仍较为有限。为弥补这一空白，本研究从Web of Science核心合集检索了2015年1月1日至2025年10月1日期间的382篇英文文献，运用文献计量方法及VOSviewer、CiteSpace和ArcGIS等分析工具，考察了发文趋势、地理分布、学科类别、主要作者、机构隶属、合作网络、主题演化及新兴研究议题。结果表明，2020年后发文量显著增长。中国、意大利和西班牙是发文量最高的国家之一，并在国际合作网络中占据重要位置。主要研究主题包括数字遗产、结构健康监测、虚拟重建、深度学习、遥感及可持续性相关应用。这些发现表明，AI相关的不可移动文物研究在工程学、保护科学、计算机科学、遥感及相关技术领域具有较强的代表性。然而，这一格局不应被解释为整个文化遗产研究领域普遍发生学科转向的证据，而是反映了本研究所采用的检索策略所捕获文献的技术导向，该策略仅限于Web of Science核心合集收录的英文文献。本研究梳理了AI在不可移动文物保护研究中的应用发展与知识结构，并提供了定量证据","Built Heritage","2026-10-15T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":326},"该文研究不可移动文化遗产领域的AI文献计量，与三农、农业信息化、智慧农业、数字乡村无关，相关性门槛未通过。",[328],{"name":323,"url":320},[330,22,331],"人工智能","文化遗产",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":334},{"candidates":335,"agricultural_evidence":14,"reason":66},[336,340,344],{"code":48,"score":111,"supported":35,"title":337,"abstract":338,"tags":339},[56],[52,55,56],[330],{"code":33,"score":34,"supported":35,"title":341,"abstract":342,"tags":343},[37],[22,37],[22],{"code":59,"score":60,"supported":35,"title":345,"abstract":346,"tags":347},[],[63,64],[],[349,350],"不可移动文物 人工智能","人工智能 文化遗产 文献计量","不可移动文物人工智能-5191","10.1186\u002Fs43238-026-00324-1",{"doi":352,"openalex_id":354,"authors":355,"venue":323,"cited_by_count":15,"oa_url":361,"card":362,"direction":163,"ingested_from":96},"W7222247878",[356,359],{"name":357,"orcid":358},"Yating Wen","https:\u002F\u002Forcid.org\u002F0009-0003-0289-3199",{"name":360,"orcid":9},"Marzudi Md Yunus","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1186\u002Fs43238-026-00324-1.pdf",{"tldr":363,"method":364,"finding":365,"direction":366,"opportunity":367},"用文献计量与知识图谱方法梳理2015-2025年AI在不可移动文化遗产研究中的应用格局。","Web of Science核心合集382篇英文文献，用VOSviewer、Ci","2020年后发文量激增，中意西三国领先，主题集中于数字遗产、结构健康监测、虚拟重建、深度学习与遥感。","其他","可借鉴其遥感与深度学习主题，探索AI在农业文化遗产、传统村落等不可移动农业遗产监测保护中的空白。","2026-10-17T23:30:41.024676Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":375,"source_url":372,"published_at":376,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":378,"sources":380,"tags":382,"primary_topic":9,"secondary_topics":385,"classification":386,"search_phrases":409,"slug":412,"view_count":413,"doi":414,"paper":415,"created_at":439},4881,"Mapping the knowledge structure and evolution of artificial intelligence for water reuse and sustainable water management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02389-4","Artificial intelligence (AI) has become a powerful instrument to solve complex problems related to sustainable water resource management because it allows creating advanced, data-driven modelling, prediction, and optimization of water systems. Although the studies in this sphere have increased quickly, the global scientific tendencies have not been thoroughly examined. This research paper presents a bibliometric review of the literature about AI applications in green water management that was published from 2015 to 2025. The Scopus database was searched with a structured search strategy on bibliographic data, and 3,280 peer-reviewed articles were analyzed. VOSviewer was used to perform bibliometric mapping and network visualization to investigate the trends in publications, most active journals, most impactful authors, contributions of institutions, country-level partnerships, and research clusters. The findings indicate that the publishing activity has grown tremendously in the last ten years, which suggests that there is a growing interest in AI-based solutions to water quality monitoring, hydrological prediction, optimization of wastewater treatment, and smart water management system. China, the United States and India became the most productive nations with the University of Chinese Academy of Sciences and Beijing normal university as some of the top contributing institutions. The analysis of the key words co-occurrence (dominant research themes) was based on machine learning, water-quality prediction, hydrological modelling, remote sensing, and decision-support systems. The co-citation analysis has shown the core studies in machine learning algorithms, including random forests, neural networks, and boosting algorithms, which underpin the present studies in this field. Overall, the results depict the growing role of AI technologies to enhance the effectiveness of water resources management and wastewater treatment. This research offers a clear insight into the intellectual framework and the future research perspective of AI-based environmentally sustainable water management which can be of great value to researchers, policymakers, and practitioners who would wish to come up with innovative and data-based water management solutions.","人工智能（AI）已成为解决可持续水资源管理相关复杂问题的有力工具，因为它能够构建先进的数据驱动模型，实现水系统的预测与优化。尽管该领域的研究迅速增加，但全球科学趋势尚未得到充分审视。本文对2015年至2025年间发表的关于人工智能在绿色水资源管理中应用的文献进行了文献计量学综述。采用结构化检索策略对Scopus数据库中的文献数据进行检索，共分析了3，280篇同行评审论文。使用VOSviewer进行文献计量制图和网络可视化，以探究发文趋势、最活跃期刊、最具影响力作者、机构贡献、国家层面合作及研究聚类。研究结果表明，过去十年间发文活动急剧增长，表明基于人工智能的水质监测、水文预测、废水处理优化和智能水管理系统解决方案日益受到关注。中国、美国和印度成为最高产国家，中国科学院和北京师范大学为贡献最多的机构之一。关键词共现分析（主导研究主题）基于机器学习、水质预测、水文建模、遥感和决策支持系统。共被引分析揭示了机器学习算法的核心研究，包括随机森林、神经网络和提升算法，这些构成了该领域当前研究的基础。总体而言，研究结果描绘了人工智能技术在提升水资源管理和废水处理效能方面日益增长的作用。本研究为基于人工智能的环境可持续水资源管理的知识框架和未来研究视角提供了清晰见解，对希望提出创新性和数据驱动型水资源管理方案的研究人员、政策制定者和实践者具有重要价值。","Discover Artificial Intelligence","2026-10-12T00:00:00Z",67,{"impact":42,"substance":111,"depth":110,"authority":112,"freshness":178,"relevant":71,"comment":379},"基于3280篇文献的AI水资源管理文献计量综述，方法规范、结论有参考价值，但属学术综述类，产业与政策落地性有限。",[381],{"name":375,"url":372},[184,63,22,383,384],"水资源管理","智慧水务",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":387},{"candidates":388,"agricultural_evidence":14,"reason":66},[389,393,401,405],{"code":48,"score":111,"supported":35,"title":390,"abstract":391,"tags":392},[56],[53,54,56],[184],{"code":394,"score":395,"supported":35,"title":396,"abstract":397,"tags":400},"H3",7,[],[398,399],"水资源","water resource",[383],{"code":59,"score":395,"supported":35,"title":402,"abstract":403,"tags":404},[],[63,64],[63],{"code":33,"score":395,"supported":35,"title":406,"abstract":407,"tags":408},[],[22,37],[22],[410,411],"AI 水资源管理 文献计量","人工智能 再生水 可持续水管理","AI水资源管理文献计量-4881",2,"10.1007\u002Fs44163-026-02389-4",{"doi":414,"openalex_id":416,"authors":417,"venue":375,"cited_by_count":15,"oa_url":432,"card":433,"direction":438,"ingested_from":96},"W7221506022",[418,421,424,426,428,430],{"name":419,"orcid":420},"Narendra Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5578-3255",{"name":422,"orcid":423},"Mangey Ram Nagar","https:\u002F\u002Forcid.org\u002F0000-0003-3635-3548",{"name":425,"orcid":9},"Isha Dharsandia",{"name":427,"orcid":9},"Ananya Tiwari",{"name":429,"orcid":9},"Santosh Kumar Mishra",{"name":431,"orcid":9},"Mohit Kumar","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44163-026-02389-4.pdf",{"tldr":434,"method":435,"finding":436,"direction":93,"opportunity":437},"对2015-2025年AI用于水回用与可持续水管理的3280篇文献做文献计量分析，梳理知识结构与演化","Scopus检索3280篇同行评议文献，用VOSviewer做共现与共被引网络可","发文量十年激增，中国、美国、印度领先；主题聚焦机器学习、水质预测、水文建模、遥感与决策支持。","可将AI水管理方法迁移至农业灌溉与面源污染场景，构建农业水-肥-碳协同的智能决策模型。","智慧农业 \u002F 农业物联网","2026-10-14T23:30:10.246538Z",{"id":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":9,"source_name":446,"source_url":443,"published_at":376,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":447,"sources":451,"tags":453,"primary_topic":9,"secondary_topics":456,"classification":457,"search_phrases":479,"slug":482,"view_count":15,"doi":483,"paper":484,"created_at":506},4880,"Development of an Explainable GeoAI Wildfire Risk Analysis Model Using Multi-Source Geospatial Data","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-17-2026","Accounting for approximately 31% of the global land surface, forests play a critical and indispensable role in sustaining ecological balance. Among the various pressures affecting forests, forest fires constitute a major environmental problem, posing significant threats to natural ecosystems while disrupting forestry activities and leading to substantial economic losses. In Türkiye, forest fires are predominantly observed in the Aegean, Mediterranean, and Marmara regions. This study presents a proactive, explainable GeoAI framework for wildfire risk analysis by integrating multi-source geospatial data. The methodology calculates a physical hazard index by evaluating advanced ensemble machine learning algorithm, specifically extreme gradient boosting (XGBoost) with the latter demonstrating superior predictive performance. The model is driven by topographic, meteorological, biotic and anthropogenic parameters. In parallel, a comprehensive vulnerability matrix encompassing physical, social, economic and environmental dimensions is constructed utilizing the Best-Worst Method (BWM). These two main components are spatially synthesized in a Geographic Information Systems (GIS) environment. The model's interpretability is enhanced focusing on feature importance to identify the primary drivers of fire ignition and spread. Results demonstrate that anthropogenic pressures and Wildland-Urban Interface (WUI) dynamics are the most significant risk determinants. The resulting high-resolution maps provide a robust Spatial Decision Support System (SDSS) for local authorities, enabling targeted mitigation strategies and contributing to the development of sustainable, smart and resilient cities.","森林约占全球陆地表面的31%，在维持生态平衡方面发挥着不可或缺的关键作用。在影响森林的诸多压力中，森林火灾构成重大环境问题，对自然生态系统造成严重威胁，同时干扰林业活动并导致重大经济损失。在土耳其，森林火灾主要发生于爱琴海、地中海和马尔马拉地区。本研究通过整合多源地理空间数据，提出了一种具有前瞻性和可解释性的地理人工智能（GeoAI）框架，用于野火风险分析。该方法通过评估先进的集成机器学习算法计算物理危险性指数，其中极端梯度提升（XGBoost）表现出更优的预测性能。模型由地形、气象、生物和人为参数驱动。与此同时，利用最优最劣法（BWM）构建了涵盖物理、社会、经济和环境维度的综合脆弱性矩阵。这两个主要组成部分在地理信息系统（GIS）环境中进行空间综合。通过聚焦特征重要性以识别火灾点燃和蔓延的主要驱动因素，增强了模型的可解释性。结果表明，人为压力和野地-城市交界区（WUI）动态是最重要的风险决定因素。所生成的高分辨率地图为地方当局提供了稳健的空间决策支持系统（SDSS），有助于制定有针对性的减缓策略，并促进可持续、智慧和韧性城市的发展。","˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences\u002FInternational archives of the photogrammetry, remote sensing and spatial information sciences",{"impact":42,"substance":448,"depth":449,"authority":112,"freshness":49,"relevant":71,"comment":450},20,17,"将可解释GeoAI与多源地理空间数据结合用于野火风险分析，方法新颖、结论明确，对农业与乡村防灾减灾有参考价值。",[452],{"name":446,"url":443},[184,454,63,455,23],"防灾减灾","森林防火",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":458},{"candidates":459,"agricultural_evidence":14,"reason":66},[460,465,471,475],{"code":48,"score":13,"supported":35,"title":461,"abstract":462,"tags":464},[],[53,54,463],"decision support",[184],{"code":41,"score":13,"supported":35,"title":466,"abstract":467,"tags":470},[],[468,44,469],"地理信息","geographic information",[23],{"code":217,"score":413,"supported":14,"title":472,"abstract":473,"tags":474},[],[],[455,454],{"code":59,"score":71,"supported":14,"title":476,"abstract":477,"tags":478},[],[],[63],[480,481],"GeoAI 野火风险 XGBoost","土耳其 森林火灾 遥感","GeoAI野火风险XGBoost-4880","10.5194\u002Fisprs-archives-xlviii-m-13-2026-17-2026",{"doi":483,"openalex_id":485,"authors":486,"venue":446,"cited_by_count":15,"oa_url":500,"card":501,"direction":438,"ingested_from":96},"W7221503678",[487,490,493,495,497],{"name":488,"orcid":489},"Esra Günaydın","https:\u002F\u002Forcid.org\u002F0000-0003-1868-0887",{"name":491,"orcid":492},"Muhammed Oğuzhan Mete","https:\u002F\u002Forcid.org\u002F0000-0002-9312-1965",{"name":494,"orcid":9},"Furkan Ali Kucuk",{"name":496,"orcid":9},"Ayça Hafızoğlu",{"name":498,"orcid":499},"Mahmut Oğuz Selbesoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-1132-3978","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F17\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-17-2026.pdf",{"tldr":502,"method":503,"finding":504,"direction":95,"opportunity":505},"构建可解释GeoAI框架，融合多源地理数据评估土耳其野火风险并制图。","XGBoost算危险指数，BWM建脆弱性矩阵，GIS空间合成。","人为压力与野火城市交界带动态是最显著的风险决定因素。","可将该可解释GeoAI框架迁移至农业火险与秸秆焚烧风险，结合时序遥感做动态预警。","2026-10-14T23:30:10.102337Z"]