[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1418":3,"related-1418":90},{"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":22,"classification":23,"search_phrases":63,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":89},1418,"AIoT-enabled urban platform for flood detection and impact mapping: towards near-real-time spatial decision support in disaster management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11069-026-08403-3","Abstract Flooding is one of the most pervasive and destructive natural hazards, with its frequency and intensity expected to worsen under climate change. While advances in geospatial analytics, Internet of Things infrastructures, and artificial intelligence have enhanced urban data ecosystems, existing smart city platforms remain limited in their capacity to provide automated, real-time flood intelligence. Most platforms focus on delineating flood extent without extending to impact assessment, leaving critical gaps in disaster response and recovery. As a result, exposure analyses of residents, buildings, and infrastructure are often conducted manually, delaying emergency services, rescue operations, and longer-term recovery planning. This study introduces an Artificial Intelligence of Things-enabled urban platform designed for flood detection, impact mapping, and spatial decision support. The conceptual architecture integrates distributed sensing, satellite imagery, and pretrained deep learning models within the Esri ArcGIS ecosystem, operationalising the principles of platform urbanism for disaster management. Demonstration of the platform draws on the March 2022 flood event in South East Queensland, with a focus on selected suburbs in Logan and the Gold Coast. Using the Prithvi-Flood Segmentation model and harmonised Sentinel-2 imagery, the workflow automates the delineation of flood extent and links outputs with exposure analytics to identify affected suburbs, railway stations, roads, buildings, and residents. Future research should focus on fine-tuning pretrained models for local contexts and scaling the architecture to incorporate additional AI-driven modules, such as road extraction, infrastructure vulnerability assessment, or population displacement modelling, thereby extending the platform’s utility across multiple hazards and governance contexts.","洪水是最普遍且破坏性最强的自然灾害之一，在气候变化背景下其发生频率和强度预计将进一步加剧。尽管地理空间分析、物联网基础设施和人工智能的进步增强了城市数据生态系统，但现有的智慧城市平台在提供自动化、实时洪水情报方面的能力仍然有限。大多数平台侧重于划定洪水范围，而未延伸至影响评估，在灾害响应和恢复方面留下了关键空白。因此，对居民、建筑物和基础设施的暴露分析往往依赖人工操作，延误了应急服务、救援行动和长期恢复规划。本研究提出了一种基于人工智能物联网（Artificial Intelligence of Things）的城市平台，旨在实现洪水检测、影响制图和空间决策支持。该概念架构在Esri ArcGIS生态系统内整合了分布式传感、卫星影像和预训练深度学习模型，将平台城市化原则应用于灾害管理实践。平台演示基于2022年3月昆士兰东南部洪水事件，重点关注洛根和黄金海岸的若干选定郊区。利用Prithvi-Flood分割模型和协调一致的Sentinel-2影像，该工作流实现了洪水范围的自动化划定，并将输出结果与暴露分析相连接，以识别受影响的郊区、火车站、道路、建筑物和居民。未来研究应侧重于针对地方情境微调预训练模型，并扩展架构以纳入更多人工智能驱动模块，如道路提取、基础设施脆弱性评估或人口迁移建模，从而将平台的适用性扩展到多种灾害和治理情境中。",null,"Natural Hazards","2026-08-31T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为城市洪涝灾害管理，不涉及三农或农业信息化，故不入选。",[19],{"name":10,"url":6},[21],"智慧城市",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":27},"needs_review","2026-10-22.2","rules",{"candidates":28,"agricultural_evidence":14,"reason":62},[29,40,48,55],{"code":30,"score":31,"supported":32,"title":33,"abstract":35,"tags":39},"I2",17,true,[34],"decision support",[36,37,38,34],"深度学习","deep learning","artificial intelligence",[],{"code":41,"score":42,"supported":32,"title":43,"abstract":44,"tags":47},"H7",6,[],[45,46],"气候变化","climate change",[],{"code":49,"score":42,"supported":32,"title":50,"abstract":51,"tags":54},"I5",[],[52,53],"物联网","internet of things",[],{"code":56,"score":57,"supported":32,"title":58,"abstract":59,"tags":61},"I4",3,[],[60],"空间分析",[],"农业相关性需要复核",[],"AIoT-enabledurbanplatformforflooddetecti-1418",2,"10.1007\u002Fs11069-026-08403-3",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":15,"oa_url":80,"card":81,"direction":87,"ingested_from":88},"W7204758566",[70,72,75,78],{"name":71,"orcid":9},"Sk Tahsin Hossain",{"name":73,"orcid":74},"Tan Yiğitcanlar","https:\u002F\u002Forcid.org\u002F0000-0001-7262-7118",{"name":76,"orcid":77},"Zhaohui Lin","https:\u002F\u002Forcid.org\u002F0000-0003-1376-3106",{"name":79,"orcid":9},"Pengjun Zhao","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11069-026-08403-3.pdf",{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"提出AIoT城市平台，自动检测洪水范围并评估影响，支持近实时灾害决策。","集成分布式传感、卫星影像与预训练深度学习模型，在ArcGIS中实现自动化洪水制图","平台在2022年昆士兰洪水案例中成功识别受影响区域、设施和居民，优于传统手动方法。","数字乡村与农业信息化","可扩展至农业领域，用于农田洪涝监测与作物损失评估，结合本地数据微调模型，提升农村灾害响应能力。","智慧农业 \u002F 农业物联网","openalex","2026-09-02T23:30:16.629291Z",{"total":65,"page":91,"page_size":65,"items":92},1,[93,169],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":109,"tags":111,"primary_topic":116,"secondary_topics":122,"classification":123,"search_phrases":143,"slug":146,"view_count":91,"doi":147,"paper":148,"created_at":168},2766,"Profiles of Structural Exclusion in the Twin Transition: Ideal Types, Political Disaffection, and Participatory Governance","https:\u002F\u002Fdoi.org\u002F10.17645\u002Fpag.12501","This article analyses how the simultaneous pursuit of green decarbonisation and digital transformation—the twin transition—generates disproportionate structural burdens on marginalised social groups. Drawing on Max Weber’s ideal‐type methodology (Idealtypus) and Spanish statistical evidence, it develops six prototypical profiles of structural exclusion across key twin‐transition sectors: urban mobility and transport, the platform and gig economy, rural energy and depopulation, digital public infrastructure and smart cities, digital agriculture, and urban renovation. Each profile identifies the mechanisms through which green and digital policies impose unequal costs on vulnerable groups, examining the resulting dynamics of institutional disaffection, potential political radicalisation, and common structural patterns. Comparative analysis identifies four recurring exclusion mechanisms—the upfront cost barrier, the digital literacy gateway, the geographic penalty, and knowledge disqualification—tracing a common pathway from structural exclusion to political disaffection. The article concludes by proposing five governance principles grounded in community work and participatory governance, offering a framework transferable to comparable EU contexts.","本文分析绿色脱碳与数字化转型的同时推进——即“双重转型”——如何对边缘化社会群体造成不成比例的结构性负担。基于马克斯·韦伯的理想类型方法（Idealtypus）与西班牙统计数据，本文在双重转型的关键领域构建了六种结构性排斥的典型画像：城市出行与交通、平台与零工经济、农村能源与人口流失、数字公共基础设施与智慧城市、数字农业以及城市更新。每种画像均识别了绿色与数字政策对弱势群体施加不平等成本的机制，并考察由此产生的制度疏离动态、潜在政治激进化以及共同的结构模式。比较分析识别出四种反复出现的排斥机制——前期成本壁垒、数字素养门槛、地理惩罚与知识 disqualification，并追溯了从结构性排斥到政治疏离的共同路径。文章最后提出五项基于社区工作与参与式治理的治理原则，提供了一个可迁移至可比欧盟情境的框架。","Politics and Governance","2026-09-17T00:00:00Z",78,{"impact":103,"substance":104,"depth":105,"authority":106,"freshness":107,"relevant":91,"comment":108},16,21,18,14,9,"以韦伯理想型方法结合西班牙统计数据，构建双转型中结构性排斥的六类原型，其中数字农业与农村能源人口流失维度对农业信息化与数字乡村治理有直接参考价值，结论与治理原则可迁移至欧盟同类情境。",[110],{"name":99,"url":96},[112,113,114,21,115],"数字农业","数字乡村","乡村治理","平台经济",{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":124,"locked":14,"version":25,"method":26,"evidence":125},"classified",{"candidates":126,"agricultural_evidence":32},[127,133,138],{"code":117,"score":128,"supported":32,"title":129,"abstract":130,"tags":132},7,[],[112,131],"digital agriculture",[112],{"code":134,"score":91,"supported":14,"title":135,"abstract":136,"tags":137},"G5",[],[],[114],{"code":139,"score":91,"supported":14,"title":140,"abstract":141,"tags":142},"I6",[],[],[113],[144,145],"乡村治理 平台经济 数字乡村 数字农业","乡村治理 平台经济","乡村治理平台经济数字乡村数字农业-2766","10.17645\u002Fpag.12501",{"doi":147,"openalex_id":149,"authors":150,"venue":99,"cited_by_count":15,"oa_url":96,"card":163,"direction":87,"ingested_from":88},"W7213460533",[151,154,157,160],{"name":152,"orcid":153},"Andrés Arias Astray","https:\u002F\u002Forcid.org\u002F0000-0001-8614-0714",{"name":155,"orcid":156},"Melisa Campana","https:\u002F\u002Forcid.org\u002F0000-0003-3988-8273",{"name":158,"orcid":159},"David Alonso González","https:\u002F\u002Forcid.org\u002F0000-0002-8876-8916",{"name":161,"orcid":162},"Juan Iglesias","https:\u002F\u002Forcid.org\u002F0000-0003-4292-5389",{"tldr":164,"method":165,"finding":166,"direction":85,"opportunity":167},"分析绿色与数字双重转型对边缘群体造成的结构性排斥，提出六种理想类型并给出治理原则。","韦伯理想类型法，结合西班牙统计数据，跨六个双转型部门比较分析。","识别出前期成本、数字素养、地理惩罚和知识剥夺四种排斥机制，导致政治疏离。","可针对数字农业中的知识剥夺与地理惩罚机制，设计参与式治理干预并评估其减贫效果。","2026-09-17T23:30:10.170805Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":9,"content":9,"source_name":174,"source_url":9,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":182,"tags":184,"primary_topic":188,"secondary_topics":194,"classification":195,"search_phrases":212,"slug":215,"view_count":57,"doi":9,"paper":216,"created_at":224},2111,"《乡村数字基础设施建设对农户消费差距的影响研究》 三项政策准自然实验","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165642.html","西北农林科技大学王月、张德胜等基于2012—2022年中国家庭追踪调查(CFPS)数据,以宽带中国、智慧城市、电商进村三项典型政策为外生冲击,构建多政策多时点渐进双重差分模型,识别乡村数字基础设施建设对农户消费差距的影响。研究发现三项政策均能显著缩小农户消费差距,电商进村政策效应相对较强;政策组合呈现\"梯度递进、功能互补、协同增效\"特征,三项组合协同效应最强。机制上通过缓解信贷约束、改善市场接入实现。","农业技术经济 2026年第05期","2026-09-05T00:00:00Z",86,{"impact":178,"substance":179,"depth":180,"authority":106,"freshness":42,"relevant":91,"comment":181},24,23,19,"基于CFPS十年数据与三项政策准自然实验，方法规范、结论可靠，对数字乡村政策协同具有参考价值，值得进入每日精选。",[183],{"name":174,"url":172},[185,113,186,21,187],"农村电商","宽带中国","农户消费",{"code":189,"slug":190,"name":191,"path":192,"parent_code":193},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":124,"locked":14,"version":25,"method":26,"evidence":196},{"candidates":197,"agricultural_evidence":32},[198,203,208],{"code":189,"score":57,"supported":32,"title":199,"abstract":200,"tags":202},[],[201],"小农户",[],{"code":204,"score":91,"supported":14,"title":205,"abstract":206,"tags":207},"E5",[],[],[185],{"code":139,"score":91,"supported":14,"title":209,"abstract":210,"tags":211},[],[],[113],[213,214],"农户消费 农村电商 宽带中国 数字乡村","农户消费 农村电商","农户消费农村电商宽带中国数字乡村-2111",{"doi":9,"openalex_id":9,"authors":217,"venue":9,"cited_by_count":15,"oa_url":9,"card":218,"direction":85,"ingested_from":223},[],{"tldr":219,"method":220,"finding":221,"direction":85,"opportunity":222},"基于CFPS数据评估三项乡村数字政策对农户消费差距的影响。","多政策多时点渐进双重差分模型，2012—2022年CFPS数据。","三项政策均显著缩小农户消费差距，电商进村效应最强，组合协同增效。","可探究数字基建缩小消费差距的长期动态与异质性，并拓展至收入、福祉等维度。","agent","2026-09-11T00:04:23.001154Z"]