[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3196":3,"related-3196":83},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":61,"slug":64,"view_count":52,"doi":65,"paper":66,"created_at":82},3196,"Geospatial and machine learning techniques for spatiotemporal analyses of urban dynamics in the Coimbatore city, South India","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fjeelm.2026.28264","Rapid urbanization has drastically changed the land use and environmental conditions in Indian cities and need to be monitored continuously for sustainable urban planning. The study used Landsat satellite images of the years 2001, 2013 and 2023 to examine the spatiotemporal urban dynamics of the Coimbatore city of South India. The impacts of urban sprawl on the environment were evaluated from Land Use\u002F Land Cover (LULC), Land Surface Temperature (LST) and spectral indices (NDVI, NDWI and NDBI). The built-up land increased by 1.21% (2001–2013) and 1.67% (2013–2023) and agricultural land decreased by 1.62% (2013–2023). The LULC classification had an Overall Accuracy of 95.76% with a Kappa coefficient of 0.95. The Kappa coefficient of 0.96 indicates that the ANN-CA model has high predictive reliability in predicting the LULC scenario in 2031. The results show that the continued expansion of cities leads to an increase in land surface temperature and a decrease in vegetation cover. This has major implications for sustainable urban development and contributes to SDG 11 and SDG 13.","快速城市化极大地改变了印度城市的土地利用和环境状况，需要持续监测以支持可持续城市规划。本研究利用2001年、2013年和2023年的Landsat卫星影像，考察了印度南部哥印拜陀市的时空城市动态。研究从土地利用\u002F土地覆盖（LULC）、地表温度（LST）和光谱指数（NDVI、NDWI和NDBI）方面评估了城市蔓延对环境的影响。建设用地在2001—2013年间增加了1.21%，在2013—2023年间增加了1.67%；农业用地在2013—2023年间减少了1.62%。LULC分类的总体精度为95.76%，Kappa系数为0.95。Kappa系数0.96表明，ANN-CA模型在预测2031年LULC情景方面具有较高的预测可靠性。结果表明，城市持续扩张导致地表温度升高和植被覆盖减少。这对可持续城市发展具有重要影响，并有助于实现可持续发展目标11和可持续发展目标13。",null,"Journal of Environmental Engineering and Landscape Management","2026-09-21T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,15,13,1,"基于Landsat多时相遥感与ANN-CA模型的印度城市扩张研究，方法规范、数据翔实，对农业用地变化与遥感监测有参考价值，但属境外区域案例，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"可持续发展","遥感","土地利用","地表温度","城市扩张",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,56],{"code":32,"score":17,"supported":45,"title":46,"abstract":48,"tags":49},true,[47],"machine learning",[],[],{"code":51,"score":52,"supported":14,"title":53,"abstract":54,"tags":55},"I3",2,[],[],[29,27],{"code":57,"score":21,"supported":14,"title":58,"abstract":59,"tags":60},"H6",[],[],[28],[62,63],"Coimbatore 城市扩张 遥感","LULC LST NDVI 印度城市","Coimbatore城市扩张遥感-3196","10.3846\u002Fjeelm.2026.28264",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":73,"oa_url":6,"card":74,"direction":80,"ingested_from":81},"W7213862636",[69,71],{"name":70,"orcid":9},"Nagamani Singamuthu",{"name":72,"orcid":9},"Elangovan Krishnan",0,{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"基于Landsat影像与机器学习分析印度哥印拜陀市2001-2023年城市扩张及其环境效应。","Landsat影像、LULC分类、NDVI\u002FNDWI\u002FNDBI指数、ANN-CA","建设用地增加、农地减少，城市扩张导致地表温度上升、植被覆盖下降。","农业遥感与作物表型","可结合多源遥感与深度学习提升城市扩张预测精度，并探究其对周边农业用地的长期影响。","农业人工智能与决策模型","openalex","2026-09-22T23:30:43.496849Z",{"total":84,"page":21,"page_size":84,"items":85},6,[86,162,230,315,381,430],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":9,"source_name":92,"source_url":89,"published_at":93,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":95,"sources":100,"tags":102,"primary_topic":105,"secondary_topics":106,"classification":107,"search_phrases":122,"slug":125,"view_count":126,"doi":127,"paper":128,"created_at":161},3369,"Understanding Land Use\u002FLand Cover Dynamics and Land Surface Temperature Variations Through Machine Learning and Geospatial Approach: A Case Study of Kullu District, Himachal Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100916","Machine learning (ML)-based classification provides an effective approach for extracting land use\u002Fland cover (LULC) information from multi-temporal satellite observations. This study evaluates Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Forest (RF) for mapping LULC changes in Kullu district, Himachal Pradesh, India, using Landsat data from 1991 to 2022. SVM achieved the highest classification accuracy and was therefore used for subsequent LULC mapping. Six LULC classes were identified and their temporal changes were examined in relation to Landsat-derived land surface temperature (LST). The built-up area increased from 4.37% in 1991 to 20.40% in 2022, whereas vegetation declined from 34.41% to 16.23% and snow cover from 15.20% to 3.02%. The area-weighted mean LST was 4.10 °C in 1991, 10.03 °C in 2001, 8.43 °C in 2011, and 4.18 °C in 2022, representing a marginal net increase of 0.08 °C over the study period. Class-wise analysis showed that barren and agricultural lands generally recorded higher mean LST, while vegetation and waterbodies exhibited comparatively lower values. The relatively high snow-cover LST in 2022 may reflect reduced and fragmented seasonal snow cover and snowmelt conditions during image acquisition. Correlation analysis revealed an inverse relationship between LST and vegetation and snow-cover indicators, with R2 values reaching 0.66 for NDVI and generally exceeding 0.62 for NDSI. Overall, the findings demonstrate substantial LULC transformation and pronounced spatial differences in surface temperature, emphasizing the role of vegetation and snow cover in regulating thermal conditions and providing useful information for sustainable land management and spatial planning in the ecologically sensitive Himalayan environment.","基于机器学习的分类为从多时相卫星观测中提取土地利用\u002F土地覆盖（LULC）信息提供了一种有效途径。本研究利用1991年至2022年的Landsat数据，评估了最大似然分类（MLC）、支持向量机（SVM）和随机森林（RF）在印度喜马偕尔邦库鲁地区LULC变化制图中的表现。SVM取得了最高的分类精度，因此被用于后续的LULC制图。研究识别出六种LULC类别，并结合Landsat反演的地表温度（LST）分析了其时间变化。建设用地面积从1991年的4.37%增至2022年的20.40%，而植被从34.41%降至16.23%，积雪覆盖从15.20%降至3.02%。面积加权平均LST在1991年为4.10 °C，2001年为10.03 °C，2011年为8.43 °C，2022年为4.18 °C，研究期间净增幅度仅为0.08 °C。分类别分析表明，裸地和农用地通常具有较高的平均LST，而植被和水体的LST相对较低。2022年积雪覆盖区LST相对较高，可能反映了影像获取时期季节性积雪覆盖减少、破碎化及融雪条件的影响。相关分析揭示了LST与植被和积雪覆盖指标之间的反比关系，NDVI的R²达到0.66，NDSI的R²普遍超过0.62。总体而言，研究结果揭示了显著的LULC转变和地表温度的明显空间差异，强调了植被和积雪覆盖在调节热力条件中的作用，并为生态敏感的喜马拉雅地区的可持续土地管理和空间规划提供了有用信息。","Atmosphere","2026-09-23T00:00:00Z",67,{"impact":17,"substance":96,"depth":97,"authority":20,"freshness":98,"relevant":21,"comment":99},20,17,9,"基于Landsat与机器学习揭示喜马拉雅地区土地利用与地表温度长期演变，数据翔实但属区域案例研究，公共影响有限。",[101],{"name":92,"url":89},[103,27,28,29,104],"机器学习","喜马拉雅",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":108},{"candidates":109,"agricultural_evidence":45},[110,114,118],{"code":32,"score":19,"supported":45,"title":111,"abstract":112,"tags":113},[47],[103,47],[103],{"code":51,"score":52,"supported":14,"title":115,"abstract":116,"tags":117},[],[],[29,27],{"code":57,"score":21,"supported":14,"title":119,"abstract":120,"tags":121},[],[],[28],[123,124],"Kullu 土地利用 地表温度","Landsat 机器学习 土地覆盖","Kullu土地利用地表温度-3369",3,"10.3390\u002Fatmos17100916",{"doi":127,"openalex_id":129,"authors":130,"venue":92,"cited_by_count":73,"oa_url":89,"card":156,"direction":80,"ingested_from":81},"W7214105064",[131,134,136,139,142,145,148,150,153],{"name":132,"orcid":133},"Sanjeev Jha","https:\u002F\u002Forcid.org\u002F0000-0002-6851-7722",{"name":135,"orcid":9},"Nirbhav",{"name":137,"orcid":138},"Atul Saini","https:\u002F\u002Forcid.org\u002F0000-0001-9840-2486",{"name":140,"orcid":141},"Toushif Jaman","https:\u002F\u002Forcid.org\u002F0000-0001-6225-5648",{"name":143,"orcid":144},"Harish Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-4738-1248",{"name":146,"orcid":147},"Pradeep Kumar Jha","https:\u002F\u002Forcid.org\u002F0000-0002-5938-1747",{"name":149,"orcid":9},"Fahdah Falah Ben Hasher",{"name":151,"orcid":152},"Abinash SİLWAL","https:\u002F\u002Forcid.org\u002F0000-0002-2285-4643",{"name":154,"orcid":155},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X",{"tldr":157,"method":158,"finding":159,"direction":78,"opportunity":160},"用机器学习与遥感分析印度库鲁区1991-2022年土地利用变化及其对地表温度的影响。","Landsat时序数据，对比MLC、SVM、随机森林分类，结合NDVI\u002FNDSI","建设用地从4.37%增至20.40%，植被与积雪大幅减少，LST净增0.08°C，植被和积雪与LST","可引入深度学习与多源遥感，在喜马拉雅生态敏感区开展LULC-LST耦合模拟与情景预测。","2026-09-24T23:30:34.225644Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":9,"source_name":168,"source_url":165,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":170,"sources":172,"tags":174,"primary_topic":176,"secondary_topics":177,"classification":183,"search_phrases":207,"slug":210,"view_count":73,"doi":211,"paper":212,"created_at":229},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":73,"substance":73,"depth":73,"authority":73,"freshness":73,"relevant":73,"comment":171},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[173],{"name":168,"url":165},[103,175,28,30],"遥感监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[178],{"code":179,"slug":180,"name":181,"path":182,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":39,"locked":14,"version":40,"method":41,"evidence":184},{"candidates":185,"agricultural_evidence":45},[186,190,197,203],{"code":32,"score":19,"supported":45,"title":187,"abstract":188,"tags":189},[47],[103,47],[103],{"code":179,"score":98,"supported":45,"title":191,"abstract":192,"tags":196},[],[193,194,195],"空间分析","GIS","spatial analysis",[],{"code":51,"score":198,"supported":45,"title":199,"abstract":200,"tags":202},7,[],[27,201],"remote sensing",[175],{"code":57,"score":21,"supported":14,"title":204,"abstract":205,"tags":206},[],[],[28],[208,209],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":211,"openalex_id":213,"authors":214,"venue":168,"cited_by_count":73,"oa_url":165,"card":224,"direction":78,"ingested_from":81},"W7223022155",[215,218,220,222],{"name":216,"orcid":217},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":219,"orcid":9},"Goanar Jal Dup",{"name":221,"orcid":9},"Weyesa Merga Chemeda",{"name":223,"orcid":9},"Shankar Karuppannan",{"tldr":225,"method":226,"finding":227,"direction":78,"opportunity":228},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":9,"source_name":236,"source_url":233,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":242,"tags":244,"primary_topic":248,"secondary_topics":249,"classification":250,"search_phrases":288,"slug":291,"view_count":52,"doi":292,"paper":293,"created_at":314},4211,"An integrated scientometric and BERTopic-based analysis on examining the role of artificial intelligence in achieving Sustainable Development Goals (SDGs)","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04841-y","This study investigates the role of Artificial Intelligence (AI) in achieving the United Nations Sustainable Development Goals (SDGs) through a comprehensive scientometric analysis. It explores the evolution of scholarly research, identifies leading contributors, maps thematic trends, examines how AI technologies contribute to advancing sustainability across multiple domains, and identifies latent semantic themes. A total of 3262 unique scholarly records published between 1989 and 2025, after deduplication and screening, were finalized from the Scopus and Web of Science databases. The dataset was analysed using Biblioshiny (R-based Bibliometrix) and VOSviewer to examine publication trends, citation impact, collaborative networks, thematic evolution, and conceptual structures. Indicators such as Relative Growth Rate (RGR), Doubling Time (DT), h-index, g-index, and Lotka’s Law were applied to evaluate research productivity and scholarly influence. In addition, BERTopic-based abstract-level topic modelling was applied to identify latent semantic themes, SDG-linked thematic domains, temporal evolution and potential research gaps. Results reveal a significant growth in AI-SDG research, particularly after 2015, with an accelerated increase post-2020. India, USA, and China emerged as the most productive contributors, while countries like USA, India, United Kingdom and Australia lead in research impact and citation quality. Thematic evolution analysis reveals a transition from foundational themes such as AI, machine learning, climate change, and decision support systems toward more integrated and sustainability-oriented research themes, including sustainable development and SDGs. Thematic linkages further demonstrate the persistent foundational role of AI and machine learning, alongside the growing centrality of climate change and sustainable development, reflecting the increasing convergence of AI research with global sustainability priorities. BERTopic analysis further revealed eleven interpretable abstract-level thematic domains after excluding one heterogeneous outlier category. The dominant themes included digital transformation and green innovation, AI-enabled education, digital health, smart agriculture, renewable energy systems, clean water and sanitation, remote sensing, NLP-based decision support, sustainable construction, tourism, and sustainable chemistry\u002Fpharmaceutical applications. This study maps the AI–SDG research landscape by integrating bibliometric indicators with thematic evolution, conceptual analysis, and abstract-level semantic topic modelling. The findings provide insights into the development and thematic orientation of AI-related research in the context of sustainable development. The use of BERTopic complements conventional bibliometric approaches by examining semantic patterns within publication abstracts alongside keyword and citation-based analyses.","本研究通过一项全面的科学计量分析，探讨了人工智能（Artificial Intelligence, AI）在实现联合国可持续发展目标（Sustainable Development Goals, SDGs）中的作用。研究考察了学术研究的演进历程，识别了主要贡献者，绘制了主题趋势图谱，分析了AI技术如何在多个领域推动可持续发展，并识别了潜在语义主题。经去重和筛选后，最终从Scopus和Web of Science数据库中确定了1989年至2025年间发表的3262条独特学术记录。使用Biblioshiny（基于R的Bibliometrix）和VOSviewer对数据集进行分析，以考察出版趋势、引用影响、合作网络、主题演进和概念结构。采用相对增长率（Relative Growth Rate, RGR）、倍增时间（Doubling Time, DT）、h指数、g指数和洛特卡定律（Lotka's Law）等指标评估研究生产力和学术影响力。此外，应用基于BERTopic的摘要级主题建模，以识别潜在语义主题、与SDG相关的主题领域、时间演变和潜在研究空白。结果显示，AI-SDG研究显著增长，尤其在2015年之后，2020年后增长加速。印度、美国和中国是最多产的贡献者，而美国、印度、英国和澳大利亚等国家在研究影响力和引用质量方面领先。主题演进分析揭示了从AI、机器学习、气候变化和决策支持系统等基础主题向更综合、面向可持续发展的研究主题（包括可持续发展和SDGs）的转变。主题关联进一步表明AI和机器学习的持续基础性作用，同时气候变化和可持续发展的中心性日益增强，反映了AI研究与全球可持续发展优先事项日益融合。BERTopic分析在排除一个异质性离群类别后，进一步揭示了十一个可解释的摘要级主题领域。主导主题包括数字化转型与绿色创新、AI赋能教育、数字健康、智慧农业、可再生能源系统、清洁水和卫生设施、遥感、基于自然语言处理的决策支持、可持续建筑、旅游业以及可持续化学\u002F制药应用。本研究通过整合文献计量","Discover Sustainability","2026-10-04T00:00:00Z",80,{"impact":18,"substance":240,"depth":18,"authority":20,"freshness":98,"relevant":21,"comment":241},22,"该研究以3262篇文献的计量与BERTopic主题建模系统梳理AI支撑SDGs的研究格局，其中智慧农业、遥感、数字健康等主题与农业信息化高度相关，方法新颖、数据规模大，值得进入每日精选。",[243],{"name":236,"url":233},[245,246,247,26,27],"数字乡村","智慧农业","农业人工智能",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":251},{"candidates":252,"agricultural_evidence":45},[253,259,265,269,276,283],{"code":32,"score":18,"supported":45,"title":254,"abstract":256,"tags":258},[255],"artificial intelligence",[103,47,255,257],"decision support",[247],{"code":260,"score":198,"supported":45,"title":261,"abstract":262,"tags":264},"I1",[],[246,263],"smart agriculture",[246],{"code":51,"score":198,"supported":45,"title":266,"abstract":267,"tags":268},[],[27,201],[27],{"code":270,"score":84,"supported":45,"title":271,"abstract":272,"tags":275},"H7",[],[273,274],"气候变化","climate change",[],{"code":277,"score":84,"supported":45,"title":278,"abstract":279,"tags":282},"I7",[],[280,281],"文献计量","bibliometric",[],{"code":284,"score":21,"supported":14,"title":285,"abstract":286,"tags":287},"I6",[],[],[245],[289,290],"AI SDG 文献计量","BERTopic 可持续农业","AISDG文献计量-4211","10.1007\u002Fs43621-026-04841-y",{"doi":292,"openalex_id":294,"authors":295,"venue":236,"cited_by_count":73,"oa_url":233,"card":308,"direction":313,"ingested_from":81},"W7219683711",[296,299,302,305],{"name":297,"orcid":298},"Reetu Verma","https:\u002F\u002Forcid.org\u002F0000-0002-0567-7219",{"name":300,"orcid":301},"Ashok Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-3279-2895",{"name":303,"orcid":304},"Kavita Pabreja","https:\u002F\u002Forcid.org\u002F0000-0001-9856-0900",{"name":306,"orcid":307},"Saurabh Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-5924-6425",{"tldr":309,"method":310,"finding":311,"direction":80,"opportunity":312},"用科学计量与BERTopic分析3262篇文献，揭示AI助力可持续发展目标的研究格局与主题演化。","Scopus\u002FWoS文献，Biblioshiny、VOSviewer计量分析加B","AI-SDG研究2015年后激增，主题从基础AI转向可持续发展，识别出智慧农业等11个主题域。","可聚焦智慧农业主题，探究AI在农业SDG中的具体贡献路径与区域差异，填补细粒度实证空白。","智慧农业 \u002F 农业物联网","2026-10-05T23:30:09.006483Z",{"id":316,"title":317,"url":318,"summary":319,"summary_zh":320,"content":9,"source_name":321,"source_url":318,"published_at":322,"category":12,"cover_url":9,"hotness":323,"is_selected":14,"score":324,"score_detail":325,"sources":328,"tags":332,"primary_topic":333,"secondary_topics":334,"classification":335,"search_phrases":363,"slug":366,"view_count":367,"doi":368,"paper":369,"created_at":380},3462,"AI For Sustainable Development Opportunities & Innovation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933761","Abstract Artificial Intelligence is increasingly being explored as a tool for accelerating progress toward sustainable development. AI can analyse large datasets, identify patterns, forecast events, optimise systems and support decision-making across agriculture, energy, water, healthcare, education, cities, industry and environmental management. Recent research shows that AI-for-SDG research is expanding rapidly, while also revealing gaps in social inclusion, governance and assessment of AI’s own environmental footprint. This project examines the major opportunities for AI-enabled sustainable development, including smart agriculture, renewable-energy optimisation, climate and disaster forecasting, intelligent waste management, sustainable cities, healthcare and education. It also discusses innovation pathways such as machine learning, computer vision, remote sensing, generative AI, digital twins and edge AI. The study emphasises that technological capability alone is insufficient: responsible AI requires reliable data, transparency, privacy, human oversight, equitable access, energy-efficient computing and lifecycle environmental assessment.","摘要 人工智能正日益被视为加速可持续发展进程的工具。人工智能可以分析大型数据集、识别模式、预测事件、优化系统，并在农业、能源、水资源、医疗、教育、城市、工业与环境管理等领域支持决策。近期研究表明，人工智能促进可持续发展目标（SDG）的研究正在迅速扩展，同时也揭示了在社会包容、治理以及人工智能自身环境足迹评估方面的不足。本项目考察了人工智能赋能可持续发展的主要机遇，包括智慧农业、可再生能源优化、气候与灾害预测、智能废物管理、可持续城市、医疗和教育。项目还讨论了机器学习、计算机视觉、遥感、生成式人工智能、数字孪生和边缘人工智能等创新路径。研究强调，仅靠技术能力是不够的：负责任的人工智能需要可靠的数据、透明度、隐私保护、人类监督、公平获取、节能计算以及生命周期环境评估。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,68,{"impact":18,"substance":326,"depth":19,"authority":20,"freshness":84,"relevant":21,"comment":327},16,"系统梳理AI赋能农业等可持续发展领域的机遇与治理挑战，属综合性研究综述，对智慧农业方向有参考价值但非突破性成果。",[329,330],{"name":321,"url":318},{"name":321,"url":331},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933762",[245,246,247,26,27],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":336},{"candidates":337,"agricultural_evidence":45},[338,343,349,353,359],{"code":260,"score":13,"supported":45,"title":339,"abstract":340,"tags":342},[],[246,341,263],"数字孪生",[246],{"code":32,"score":13,"supported":45,"title":344,"abstract":345,"tags":348},[],[103,346,47,255,347],"计算机视觉","computer vision",[247],{"code":51,"score":198,"supported":45,"title":350,"abstract":351,"tags":352},[],[27,201],[27],{"code":354,"score":126,"supported":45,"title":355,"abstract":356,"tags":358},"H3",[],[357],"水资源",[],{"code":284,"score":21,"supported":14,"title":360,"abstract":361,"tags":362},[],[],[245],[364,365],"AI 可持续发展 智慧农业","农业人工智能 可持续发展 数字乡村 智慧农业","AI可持续发展智慧农业-3462",4,"10.5281\u002Fzenodo.22933761",{"doi":368,"openalex_id":370,"authors":371,"venue":321,"cited_by_count":73,"oa_url":318,"card":374,"direction":313,"ingested_from":81},"W7214172367",[372],{"name":373,"orcid":9},"Saloni Ananda Patil",{"tldr":375,"method":376,"finding":377,"direction":378,"opportunity":379},"综述AI在可持续发展各领域的机会与创新路径，并强调负责任AI的治理要求。","文献综述，覆盖机器学习、计算机视觉、遥感、数字孪生与边缘AI等。","AI-for-SDG研究快速扩张，但社会包容、治理与AI自身环境足迹评估仍存缺口。","农业绿色发展与碳","可量化AI自身能耗与碳足迹，并评估其在农业减排中的净环境效益。","2026-09-25T23:30:08.894307Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":9,"source_name":387,"source_url":384,"published_at":388,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":389,"sources":391,"tags":393,"primary_topic":395,"secondary_topics":396,"classification":397,"search_phrases":413,"slug":416,"view_count":367,"doi":417,"paper":418,"created_at":429},3015,"Forecasting Urbanization Dynamics On İstanbul’s European Side Using Deep Learning And Extrapolation Techniques","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0037","Abstract Rapid urbanization in metropolitan regions poses significant environmental, social, and infrastructural challenges, necessitating advanced analytical approaches to monitor and predict urban growth. This study investigates the spatio-temporal dynamics of urbanization on the European side of Istanbul from 2013 to 2024 using Landsat 8 imagery and a deep learning (DL)–based Land Cover Classification model integrated within ArcGIS Pro. The U-Net–based pre-trained model generated 15-class Land Use\u002FLand Cover (LULC) maps, which were validated against the Urban Atlas dataset, resulting in high classification accuracies for forest and water classes (PA: 0.84–0.94; UA: 0.87–0.87) and an overall binary urban\u002Fnon-urban accuracy of 87 %, confirming the robustness of the employed DL approach. Spatio-temporal analyses of LULC data were conducted using both Ordinary Least Squares (OLS) and nonlinear regression functions to examine urban growth trends and project future development for 2025, 2026, and 2027. The results indicate a strong linear increase in urbanized areas across most districts, with total developed area on the European side projected to reach approximately 807 km² by 2027, representing a nearly 50% increase compared to 2013. These findings highlight the significant pressure of urban expansion on natural and agricultural lands and emphasize the need for informed planning strategies. By integrating remote sensing, deep learning, and predictive modeling, this study provides actionable insights for sustainable urban development, offering a replicable framework for monitoring rapid urbanization and supporting policy decisions to mitigate environmental and socio-spatial impacts in rapidly growing metropolitan regions.","摘要 大都市区域的快速城市化带来了显著的环境、社会和基础设施挑战，亟需先进的分析方法来监测和预测城市增长。本研究利用Landsat 8影像和集成于ArcGIS Pro中的基于深度学习（DL）的土地覆盖分类模型，研究了2013年至2024年伊斯坦布尔欧洲一侧城市化的时空动态。基于U-Net的预训练模型生成了15类土地利用\u002F土地覆盖（LULC）地图，并依据Urban Atlas数据集进行了验证，森林和水体类别的分类精度较高（生产者精度PA：0.84–0.94；用户精度UA：0.87–0.87），城市\u002F非城市二分类总体精度达87%，证实了所采用深度学习方法稳健可靠。研究采用普通最小二乘法（OLS）和非线性回归函数对LULC数据进行时空分析，以考察城市增长趋势并预测2025年、2026年和2027年的未来发展。结果表明，大多数区域的城市化面积呈显著线性增长，预计到2027年欧洲一侧的总建成区面积将达到约807 km²，较2013年增长近50%。这些发现凸显了城市扩张对自然和农业用地的巨大压力，并强调了科学规划策略的必要性。通过整合遥感、深度学习和预测建模，本研究为可持续城市发展提供了可操作的见解，为监测快速城市化提供了一个可复制的框架，并支持旨在缓解快速增长的都市区域中环境和社会空间影响的政策决策。","Journal of Landscape Ecology","2026-09-19T00:00:00Z",{"impact":73,"substance":73,"depth":73,"authority":73,"freshness":73,"relevant":73,"comment":390},"研究伊斯坦布尔城市扩张与土地覆盖预测，属城市遥感与景观生态领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[392],{"name":387,"url":384},[394,175,28,30],"深度学习",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":398},{"candidates":399,"agricultural_evidence":45},[400,405,409],{"code":32,"score":19,"supported":45,"title":401,"abstract":403,"tags":404},[402],"deep learning",[394,402],[394],{"code":51,"score":198,"supported":45,"title":406,"abstract":407,"tags":408},[],[27,201],[175],{"code":57,"score":21,"supported":14,"title":410,"abstract":411,"tags":412},[],[],[28],[414,415],"伊斯坦布尔 城市扩张 遥感","U-Net 土地覆盖分类","伊斯坦布尔城市扩张遥感-3015","10.2478\u002Fjlecol-2026-0037",{"doi":417,"openalex_id":419,"authors":420,"venue":387,"cited_by_count":73,"oa_url":384,"card":424,"direction":78,"ingested_from":81},"W7213670913",[421],{"name":422,"orcid":423},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":425,"method":426,"finding":427,"direction":78,"opportunity":428},"用Landsat 8影像和U-Net深度学习模型分析伊斯坦布尔欧洲侧2013-2024年城市化动态并","Landsat 8影像、ArcGIS Pro中U-Net预训练模型生成15类LU","城市面积呈强线性增长，2027年预计达807 km²，较2013年增长近50%，挤压自然与农业用地。","可借鉴该遥感+深度学习+外推框架，研究快速城市化对城郊农业用地与耕地保护的时空影响。","2026-09-20T23:30:21.335156Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":436,"source_url":433,"published_at":437,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":438,"sources":440,"tags":442,"primary_topic":444,"secondary_topics":445,"classification":446,"search_phrases":463,"slug":466,"view_count":52,"doi":467,"paper":468,"created_at":500},1733,"Machine Learning-Based Assessment of Land-Use Change, Forest Recovery, and Landscape Connectivity in Islamabad","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091641","LULCC is a major driver of habitat fragmentation, biodiversity loss, and declining landscape connectivity, particularly in rapidly urbanizing regions. Although Islamabad has experienced substantial urban expansion and forest-cover change, long-term city-scale assessments linking land-cover dynamics with landscape connectivity remain limited. This study examined spatiotemporal LULCC in Islamabad from 1991 to 2021 and assessed whether recent forest recovery improved landscape structural connectivity. Landsat images acquired in 1991, 2001, 2011, and 2021 were classified into five land-cover categories: water, forest, built-up area, bare land, and agricultural land. Classification was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE). Landscape composition and spatial configuration were quantified using FRAGSTATS 4.3, while forest fragmentation was evaluated using the Landscape Fragmentation Tool v2.0 (LFT) with a 100 m edge threshold. The classifications achieved overall accuracies above 90%, with Kappa coefficients (K) greater than 0.85. Built-up area increased from 76.31 km2, representing 7.55% of the study area, in 1991, to 258.62 km2, or 25.60%, in 2021, demonstrating rapid urban expansion and associated habitat conversion. Forest cover increased to 340.86 km2 in 2001, declined to 271.96 km2 in 2011, and subsequently recovered to 409.22 km2 in 2021. Despite this increase in forest extent, fragmentation metrics indicated persistent spatial subdivision and limited structural connectivity. High patch density (PD), reduced landscape aggregation, and changes in the largest patch index (LPI) indicated persistent spatial subdivision and limited habitat continuity. These findings highlight the value of integrating RF-based land-cover classification, multitemporal remote sensing, and landscape metrics for urban environmental monitoring. The findings suggest that future land-use planning should consider landscape connectivity, protection of existing forest patches, and spatially coordinated restoration alongside continued reforestation.","土地利用\u002F土地覆盖变化（LULCC）是栖息地破碎化、生物多样性丧失及景观连通性下降的主要驱动因素，尤其在快速城市化地区表现突出。尽管伊斯兰堡经历了显著的城市扩张和森林覆盖变化，但将土地覆盖动态与景观连通性相结合的城市尺度长期评估仍较为有限。本研究分析了1991年至2021年间伊斯兰堡土地利用\u002F土地覆盖变化的时空特征，并评估了近期森林恢复是否改善了景观结构连通性。将1991年、2001年、2011年和2021年获取的Landsat影像划分为五类土地覆盖类型：水体、森林、建设用地、裸地和农业用地。分类采用谷歌地球引擎（GEE）中的随机森林（RF）算法完成。景观组成和空间配置通过FRAGSTATS 4.3进行量化，森林破碎化则利用景观破碎化工具v2.0（LFT）以100米边缘阈值进行评估。分类结果的总体精度超过90%，Kappa系数（K）大于0.85。建设用地面积从1991年的76.31平方公里（占研究区面积的7.55%）增加到2021年的258.62平方公里（占25.60%），表明城市快速扩张及随之而来的栖息地转化。森林覆盖面积在2001年增至340.86平方公里，2011年降至271.96平方公里，随后于2021年恢复至409.22平方公里。尽管森林面积有所增加，破碎化指标仍表明存在持续的空间分割和有限的结构连通性。高斑块密度（PD）、景观聚集度降低以及最大斑块指数（LPI）的变化均反映出持续的空间分割和有限的栖息地连续性。这些发现凸显了将基于随机森林的土地覆盖分类、多时相遥感与景观指标相结合用于城市环境监测的价值。研究结果表明，未来的土地利用规划应综合考虑景观连通性、现有森林斑块的保护以及空间协调的恢复措施，同时持续推进植树造林。","Land","2026-09-04T00:00:00Z",{"impact":73,"substance":73,"depth":73,"authority":73,"freshness":73,"relevant":73,"comment":439},"研究伊斯兰堡城市扩张与森林恢复，非中国三农或农业信息化主题，不入选。",[441],{"name":436,"url":433},[27,28,443],"生态",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":447},{"candidates":448,"agricultural_evidence":45},[449,453,459],{"code":32,"score":17,"supported":45,"title":450,"abstract":451,"tags":452},[47],[],[],{"code":57,"score":198,"supported":45,"title":454,"abstract":455,"tags":458},[],[456,457],"生物多样性","biodiversity",[28],{"code":51,"score":198,"supported":45,"title":460,"abstract":461,"tags":462},[],[27,201],[27],[464,465],"土地利用 生态 遥感","土地利用 生态","土地利用生态遥感-1733","10.3390\u002Fland15091641",{"doi":467,"openalex_id":469,"authors":470,"venue":436,"cited_by_count":73,"oa_url":433,"card":495,"direction":78,"ingested_from":81},"W7208716166",[471,474,476,478,481,484,487,490,493],{"name":472,"orcid":473},"Muhammad Tariq Badshah","https:\u002F\u002Forcid.org\u002F0000-0003-4447-4091",{"name":475,"orcid":9},"Hakim Ullah Khan",{"name":477,"orcid":9},"Muhammad Shabir",{"name":479,"orcid":480},"Shahid Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-4343-7187",{"name":482,"orcid":483},"Khadim Hussain","https:\u002F\u002Forcid.org\u002F0000-0003-3351-3438",{"name":485,"orcid":486},"Farhan Amin","https:\u002F\u002Forcid.org\u002F0000-0002-6385-5511",{"name":488,"orcid":489},"Isabel de la Torre Díez","https:\u002F\u002Forcid.org\u002F0000-0003-3134-7720",{"name":491,"orcid":492},"Mirtha Silvana Garat de Marín","https:\u002F\u002Forcid.org\u002F0000-0003-3044-8087",{"name":494,"orcid":9},"Eduardo Silva Alvarado",{"tldr":496,"method":497,"finding":498,"direction":78,"opportunity":499},"利用机器学习评估伊斯兰堡1991-2021年土地利用变化、森林恢复及景观连通性。","随机森林分类Landsat影像，FRAGSTATS和LFT分析景观格局。","城市扩张显著，森林面积先降后升，但景观破碎化持续，连通性有限。","可延伸研究森林恢复与景观连通性的空间协调机制，结合多尺度数据优化城市生态规划。","2026-09-05T23:30:27.381903Z"]