[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3015":3,"related-3015":79},{"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":25,"secondary_topics":31,"classification":32,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":78},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。",null,"Journal of Landscape Ecology","2026-09-19T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"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。",[19],{"name":10,"url":6},[21,22,23,24],"深度学习","遥感监测","土地利用","城市扩张",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"classified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":40},[38,45,53],{"code":26,"score":39,"supported":40,"title":41,"abstract":43,"tags":44},15,true,[42],"deep learning",[21,42],[21],{"code":46,"score":47,"supported":40,"title":48,"abstract":49,"tags":52},"I3",7,[],[50,51],"遥感","remote sensing",[22],{"code":54,"score":55,"supported":14,"title":56,"abstract":57,"tags":58},"H6",1,[],[],[23],[60,61],"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",4,"10.2478\u002Fjlecol-2026-0037",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":15,"oa_url":6,"card":71,"direction":75,"ingested_from":77},"W7213670913",[68],{"name":69,"orcid":70},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"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。","农业遥感与作物表型","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。","openalex","2026-09-20T23:30:21.335156Z",{"total":80,"page":55,"page_size":80,"items":81},6,[82,150,210,264,315,408],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":9,"source_name":88,"source_url":85,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":90,"sources":92,"tags":94,"primary_topic":96,"secondary_topics":97,"classification":103,"search_phrases":127,"slug":130,"view_count":15,"doi":131,"paper":132,"created_at":149},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":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":91},"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。",[93],{"name":88,"url":85},[95,22,23,24],"机器学习",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[98],{"code":99,"slug":100,"name":101,"path":102,"parent_code":30},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":33,"locked":14,"version":34,"method":35,"evidence":104},{"candidates":105,"agricultural_evidence":40},[106,111,119,123],{"code":26,"score":39,"supported":40,"title":107,"abstract":109,"tags":110},[108],"machine learning",[95,108],[95],{"code":99,"score":112,"supported":40,"title":113,"abstract":114,"tags":118},9,[],[115,116,117],"空间分析","GIS","spatial analysis",[],{"code":46,"score":47,"supported":40,"title":120,"abstract":121,"tags":122},[],[50,51],[22],{"code":54,"score":55,"supported":14,"title":124,"abstract":125,"tags":126},[],[],[23],[128,129],"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":131,"openalex_id":133,"authors":134,"venue":88,"cited_by_count":15,"oa_url":85,"card":144,"direction":75,"ingested_from":77},"W7223022155",[135,138,140,142],{"name":136,"orcid":137},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":139,"orcid":9},"Goanar Jal Dup",{"name":141,"orcid":9},"Weyesa Merga Chemeda",{"name":143,"orcid":9},"Shankar Karuppannan",{"tldr":145,"method":146,"finding":147,"direction":75,"opportunity":148},"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":151,"title":152,"url":153,"summary":154,"summary_zh":155,"content":9,"source_name":156,"source_url":153,"published_at":157,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":158,"sources":160,"tags":162,"primary_topic":165,"secondary_topics":166,"classification":167,"search_phrases":183,"slug":186,"view_count":187,"doi":188,"paper":189,"created_at":209},4607,"Topographic gradients shape territorial-space evolution and their nonlinear drivers in a karst mountain region of Southwest China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74236-6","Abstract Karst mountainous areas are characterised by fragmented terrain and high ecological sensitivity, which make territorial-space evolution strongly terrain dependent. However, existing studies often treat terrain merely as a general control variable, leaving the spatial transitions and nonlinear driving mechanisms across different terrain gradients insufficiently understood. Taking the karst mountainous area of northwestern Guangxi as a case study, this study integrates land-use, natural environmental, socioeconomic and locational data from 1980 to 2020. A transition matrix, terrain distribution index (TDI), and explainable machine-learning models were employed to identify the terrain-gradient effects and driving mechanisms of territorial-space evolution. The results show that vegetation-dominated ecological space (VES) consistently accounted for more than 80% of the study area. Nevertheless, the study period was accompanied by the continuous expansion of urban space (US), whose area increased by approximately 397.66% from 1980 to 2020, while ecological space (ES) and agricultural space (AS) declined overall. Transitions from AS to ES were mainly concentrated in middle- to high-elevation and relatively steep areas, whereas transitions from AS to US were clustered in low-elevation and gentle-slope areas. Aquatic ecological space (AES), US and AS tended to occur in low-elevation and gentle-slope areas, whereas VES was dominant in high-elevation and steep-slope areas. The random forest (RF) model performed best, with an R 2 of 0.791 on the test set, outperforming ordinary least squares (OLS) and extreme gradient boosting (XGBoost). Shapley additive explanations (SHAP) analysis based on the RF model further indicated that the normalised difference vegetation index (NDVI), population density (POP) and distance to rivers (DRIV) were the leading explanatory factors. The dominant roles of natural environmental, socioeconomic and locational accessibility factors shifted across elevation and slope gradients and exhibited clear threshold effects. These findings show how terrain gradients are associated with territorial-space patterns, transition pathways and driving mechanisms, and provide a scientific basis for territorial-space optimisation in karst mountainous areas.","喀斯特山区地形破碎、生态敏感性强，其国土空间演变具有强烈的地形依赖性。然而，现有研究多将地形视为一般性控制变量，对不同地形梯度下国土空间转换过程及非线性驱动机制的认识仍不充分。本研究以桂西北喀斯特山区为案例，整合1980—2020年土地利用、自然环境、社会经济和区位数据，采用转移矩阵、地形分布指数（TDI）和可解释机器学习模型，识别国土空间演变的地形梯度效应与驱动机制。结果表明：以植被为主的生态空间（VES）始终占研究区面积的80%以上；但研究期内城镇空间（US）持续扩张，1980—2020年面积增加约397.66%，而生态空间（ES）和农业空间（AS）总体下降。AS向ES的转换主要集中于中高海拔和较陡区域，而AS向US的转换则聚集在低海拔和缓坡区域。水域生态空间（AES）、US和AS倾向于分布在低海拔和缓坡区域，而VES则在高海拔和陡坡区域占主导。随机森林（RF）模型表现最佳，测试集R²为0.791，优于普通最小二乘法（OLS）和极端梯度提升（XGBoost）。基于RF模型的SHAP（Shapley加性解释）分析进一步表明，归一化植被指数（NDVI）、人口密度（POP）和距河流距离（DRIV）是主要解释因子。自然环境、社会经济和区位可达性因子的主导作用随海拔和坡度梯度发生转变，并表现出明显的阈值效应。上述发现揭示了地形梯度与国土空间格局、转换路径及驱动机制之间的关联，可为喀斯特山区国土空间优化提供科学依据。","Scientific Reports","2026-10-09T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":159},"该论文聚焦喀斯特山区国土空间演化的地形梯度与非线性驱动机制，属土地利用与景观生态研究，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。",[161],{"name":156,"url":153},[22,23,163,164],"喀斯特山区","国土空间",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":168},{"candidates":169,"agricultural_evidence":40},[170,175,179],{"code":26,"score":171,"supported":40,"title":172,"abstract":173,"tags":174},3,[],[95],[],{"code":54,"score":55,"supported":14,"title":176,"abstract":177,"tags":178},[],[],[23],{"code":46,"score":55,"supported":14,"title":180,"abstract":181,"tags":182},[],[],[22],[184,185],"桂西北 喀斯特 国土空间演化","喀斯特山区 国土空间 土地利用 遥感监测","桂西北喀斯特国土空间演化-4607",2,"10.1038\u002Fs41598-026-74236-6",{"doi":188,"openalex_id":190,"authors":191,"venue":156,"cited_by_count":15,"oa_url":153,"card":203,"direction":208,"ingested_from":77},"W7221135432",[192,194,196,198,200],{"name":193,"orcid":9},"Xiangying Kong",{"name":195,"orcid":9},"Baoqing Hu",{"name":197,"orcid":9},"Yurou Liang",{"name":199,"orcid":9},"Jiaxin Li",{"name":201,"orcid":202},"Xuan Zhong","https:\u002F\u002Forcid.org\u002F0009-0000-1258-4363",{"tldr":204,"method":205,"finding":206,"direction":75,"opportunity":207},"分析桂西北喀斯特山区1980-2020年国土空间演变的地形梯度效应与非线性驱动机制。","土地利用转移矩阵、地形分布指数与可解释机器学习（随机森林+SHAP）。","城镇空间增约397.66%，农业转生态集中于中高海拔陡坡，NDVI、人口密度和距河距离为主导驱动且存","可将地形梯度阈值与可解释机器学习结合，用于喀斯特山区国土空间优化与生态修复分区。","农业人工智能与决策模型","2026-10-10T23:30:50.166440Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":9,"source_name":216,"source_url":213,"published_at":217,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":224,"tags":226,"primary_topic":229,"secondary_topics":230,"classification":231,"search_phrases":246,"slug":249,"view_count":187,"doi":250,"paper":251,"created_at":263},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。","Journal of Environmental Engineering and Landscape Management","2026-09-21T00:00:00Z",62,{"impact":220,"substance":221,"depth":39,"authority":222,"freshness":220,"relevant":55,"comment":223},8,18,13,"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。",[225],{"name":216,"url":213},[227,50,23,228,24],"可持续发展","地表温度",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":232},{"candidates":233,"agricultural_evidence":40},[234,238,242],{"code":26,"score":220,"supported":40,"title":235,"abstract":236,"tags":237},[108],[],[],{"code":46,"score":187,"supported":14,"title":239,"abstract":240,"tags":241},[],[],[228,50],{"code":54,"score":55,"supported":14,"title":243,"abstract":244,"tags":245},[],[],[23],[247,248],"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":250,"openalex_id":252,"authors":253,"venue":216,"cited_by_count":15,"oa_url":213,"card":258,"direction":208,"ingested_from":77},"W7213862636",[254,256],{"name":255,"orcid":9},"Nagamani Singamuthu",{"name":257,"orcid":9},"Elangovan Krishnan",{"tldr":259,"method":260,"finding":261,"direction":75,"opportunity":262},"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。","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。","2026-09-22T23:30:43.496849Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":272,"sources":274,"tags":276,"primary_topic":277,"secondary_topics":278,"classification":279,"search_phrases":290,"slug":293,"view_count":187,"doi":294,"paper":295,"created_at":314},2440,"Mapping urban expansion and land transformation in Dhaka, Bangladesh by fusing night-time lights, thermal, and spectral data via machine learning approaches","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cacint.2026.100472","This study evaluates urban expansion and land transformation in Dhaka District, Bangladesh, between 2016 and 2024 by integrating multi-source geospatial data with machine learning. The aim was to map urban growth more precisely than conventional spectral indices allow and to characterize its spatial patterns through landscape metrics and change detection. Landsat 8 thermal and spectral data were fused with Visible Infrared Imaging Radiometer Suite (VIIRS) night-time lights, a suite of spectral indices (NDVI, NDBI, SAVI, MNDWI), Gray-Level Co-occurrence Matrix (GLCM) texture features, and road-network data. Three supervised classifiers, Random Forest (RF), Support Vector Machine (SVM), and Gradient Tree Boosting (GTB), together with a majority-voting ensemble, were compared. Random Forest performed best in every study year (overall accuracy 99.57–99.62%; Kappa 0.991–0.992) and was adopted as the urban layer for all subsequent analyses. The urban area expanded by 39.53 km 2 over the period. Vegetation and agricultural land were the predominant source of new urban land (75.6%), whereas direct water-to-urban conversion was minor (below 2%); total water-body area nonetheless declined by roughly 25% (from 79.0 to 58.9 km 2 ), largely through indirect conversion to vegetation and seasonal land rather than through direct urbanization. Landscape metrics indicate increasingly fragmented growth, with patch density rising from 2.46 to 7.82 per 100 ha and Shannon's entropy from 0.45 to 0.49. Zonal analysis identifies the northeastern and southeastern peri -urban fringes as the fastest-growing, dominated by leapfrog and edge development. The findings depict an unsustainable expansion trajectory whose environmental costs stem from the direct loss of vegetation and agricultural land alongside indirect pressure on floodplain water bodies, and they offer a transferable multi-modal framework for monitoring sprawl in Global South megacities. The application of the machine learning and geospatial approach in this study will help urban planners, policymakers, and stakeholders manage Dhaka's rapid and fragmented urban growth.","本研究通过整合多源地理空间数据与机器学习方法，评估了2016年至2024年间孟加拉国达卡地区的城市扩张与土地转型。研究旨在比传统光谱指数更精确地绘制城市增长图景，并通过景观格局指标与变化检测刻画其空间模式。研究将Landsat 8热红外与光谱数据与可见光红外成像辐射仪套件（VIIRS）夜间灯光数据、一组光谱指数（NDVI、NDBI、SAVI、MNDWI）、灰度共生矩阵（GLCM）纹理特征以及道路网络数据进行融合。比较了三种监督分类器——随机森林（RF）、支持向量机（SVM）和梯度树提升（GTB），以及多数投票集成方法。随机森林在各研究年份均表现最佳（总体精度99.57%–99.62%；Kappa系数0.991–0.992），并被采纳为后续所有分析的城市图层。研究期内城市面积扩张了39.53 km²。植被和农业用地是新增城市用地的主要来源（75.6%），而直接的水体向城市用地转化较少（低于2%）；然而，水体总面积仍下降了约25%（从79.0 km²降至58.9 km²），这主要是通过间接转化为植被和季节性土地而非直接城市化实现的。景观格局指标表明增长日益破碎化，斑块密度从每100 ha 2.46上升至7.82，香农熵从0.45上升至0.49。分区分析识别出东北部和东南部城市边缘区为增长最快的区域，以跳跃式和边缘式开发为主。研究结果描绘了一条不可持续的扩张轨迹，其环境代价既来自植被和农业用地的直接丧失，也来自对洪泛平原水体的间接压力，并为监测全球南方特大城市蔓延提供了一个可迁移的多模态框架。本研究中的机器学习与地理空间方法的应用将有助于城市规划者、政策制定者和利益相关者管理达卡快速且破碎化的城市增长。","City and Environment Interactions","2026-09-11T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":273},"研究孟加拉国达卡城市扩张与土地转化，属城市遥感与城市规划领域，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。",[275],{"name":270,"url":267},[95,22,24],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":280},{"candidates":281,"agricultural_evidence":40},[282,286],{"code":26,"score":39,"supported":40,"title":283,"abstract":284,"tags":285},[108],[95,108],[95],{"code":46,"score":55,"supported":14,"title":287,"abstract":288,"tags":289},[],[],[22],[291,292],"城市扩张 机器学习 遥感监测","城市扩张 机器学习","城市扩张机器学习遥感监测-2440","10.1016\u002Fj.cacint.2026.100472",{"doi":294,"openalex_id":296,"authors":297,"venue":270,"cited_by_count":15,"oa_url":267,"card":309,"direction":208,"ingested_from":77},"W7212257463",[298,300,303,305,307],{"name":299,"orcid":9},"Arpon Sarkar",{"name":301,"orcid":302},"Mafrid Haydar","https:\u002F\u002Forcid.org\u002F0009-0003-9229-0171",{"name":304,"orcid":9},"Farzana Islam Mitu",{"name":306,"orcid":9},"Al Hossain Rafi",{"name":308,"orcid":9},"Sakib Hosan",{"tldr":310,"method":311,"finding":312,"direction":75,"opportunity":313},"融合夜间灯光、热红外与光谱数据，用机器学习精准监测达卡2016-2024年城市扩张与土地转化。","Landsat 8热红外与光谱、VIIRS夜间灯光、NDVI\u002FNDBI等指数、G","城市扩张39.53 km²，75.6%来自植被和农地，水体间接减少约25%，增长日益破碎化。","可将该多模态融合框架迁移至耕地流失预警与城郊农地保护，结合时序模型预测扩张对农业的长期影响。","2026-09-14T23:30:47.470495Z",{"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":13,"is_selected":14,"score":323,"score_detail":324,"sources":329,"tags":331,"primary_topic":335,"secondary_topics":341,"classification":349,"search_phrases":384,"slug":387,"view_count":15,"doi":388,"paper":389,"created_at":407},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":325,"substance":326,"depth":327,"authority":222,"freshness":13,"relevant":55,"comment":328},16,21,17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[330],{"name":321,"url":318},[332,95,22,333,334],"智慧农业","土壤墒情","冬小麦",{"code":336,"slug":337,"name":338,"path":339,"parent_code":340},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[342,348],{"code":343,"slug":344,"name":345,"path":346,"parent_code":347},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":33,"locked":14,"version":34,"method":35,"evidence":350},{"candidates":351,"agricultural_evidence":40},[352,358,363,367,372,379],{"code":336,"score":353,"supported":40,"title":354,"abstract":356,"tags":357},26,[355,337],"winter wheat",[338,355,337],[334],{"code":343,"score":39,"supported":40,"title":359,"abstract":360,"tags":362},[344],[361,344],"土壤",[333],{"code":26,"score":39,"supported":40,"title":364,"abstract":365,"tags":366},[108],[95,108],[95],{"code":46,"score":13,"supported":40,"title":368,"abstract":369,"tags":371},[],[50,51,370],"multispectral",[22],{"code":373,"score":80,"supported":40,"title":374,"abstract":375,"tags":378},"H3",[],[376,377],"灌溉","irrigation",[],{"code":380,"score":63,"supported":40,"title":381,"abstract":382,"tags":383},"I1",[],[332],[332],[385,386],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":388,"openalex_id":390,"authors":391,"venue":321,"cited_by_count":15,"oa_url":318,"card":401,"direction":406,"ingested_from":77},"W7223156204",[392,395,397,399],{"name":393,"orcid":394},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":396,"orcid":9},"Yi Zhang",{"name":398,"orcid":9},"WEI Lei",{"name":400,"orcid":9},"Chengcai Zhang",{"tldr":402,"method":403,"finding":404,"direction":75,"opportunity":405},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":409,"title":410,"url":411,"summary":412,"summary_zh":413,"content":9,"source_name":414,"source_url":411,"published_at":415,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":416,"score_detail":417,"sources":420,"tags":422,"primary_topic":426,"secondary_topics":432,"classification":434,"search_phrases":452,"slug":455,"view_count":15,"doi":456,"paper":457,"created_at":471},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":418,"substance":221,"depth":325,"authority":13,"freshness":220,"relevant":55,"comment":419},12,"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[421],{"name":414,"url":411},[332,423,21,424,425],"农业人工智能","番茄病害","图像识别",{"code":427,"slug":428,"name":429,"path":430,"parent_code":431},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[433],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":33,"locked":14,"version":34,"method":35,"evidence":435},{"candidates":436,"agricultural_evidence":40},[437,443,447],{"code":427,"score":39,"supported":40,"title":438,"abstract":440,"tags":442},[439],"tomato",[441,439],"番茄",[424],{"code":26,"score":112,"supported":40,"title":444,"abstract":445,"tags":446},[],[21,42],[423,425,21],{"code":380,"score":47,"supported":40,"title":448,"abstract":449,"tags":451},[],[332,450],"smart agriculture",[332],[453,454],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":456,"openalex_id":458,"authors":459,"venue":414,"cited_by_count":15,"oa_url":465,"card":466,"direction":406,"ingested_from":77},"W7222977034",[460,462],{"name":461,"orcid":9},"Mrs. Vijetalaxmi A Hegde",{"name":463,"orcid":464},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":467,"method":468,"finding":469,"direction":208,"opportunity":470},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","2026-10-20T23:30:10.424511Z"]