[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3196":3,"related-3196":53},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":52},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",[32,33],"Coimbatore 城市扩张 遥感","LULC LST NDVI 印度城市","Coimbatore城市扩张遥感-3196",0,"10.3846\u002Fjeelm.2026.28264",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":44,"direction":50,"ingested_from":51},"W7213862636",[40,42],{"name":41,"orcid":9},"Nagamani Singamuthu",{"name":43,"orcid":9},"Elangovan Krishnan",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"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":54,"page":21,"page_size":54,"items":55},6,[56,111,148,194,236,265],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":61,"content":9,"source_name":62,"source_url":59,"published_at":63,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":65,"sources":70,"tags":72,"search_phrases":76,"slug":79,"view_count":35,"doi":80,"paper":81,"created_at":110},3185,"Generating Annual 10 m Land Cover Maps for 37 Chinese Metropolises Using Google Satellite Embeddings","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183233","Accurate urban land cover information is essential for monitoring urbanization and environmental change, yet the complexity and heterogeneity of urban landscapes remain challenging for high-resolution remote sensing classification. This study used Google Satellite Embeddings as the core feature input, combined with ensemble learning and spatiotemporal post-processing, to generate annual 10 m land cover maps for 37 Chinese metropolises from 2017 to 2024. Results showed that the weighted ensemble model achieved an overall accuracy (OA) of 81.99%, 2.13% higher than the best individual model, while spatiotemporal post-processing further increased OA to 82.59%. Compared with existing land cover products such as FROM-GLC10, the proposed product achieved 4.20% higher OA. The ablation experiment showed that the embedding representation was the primary source of performance improvement. Under the same ensemble learning framework, Satellite Embedding features outperformed conventional remote sensing features by 6.54% in OA. SHapley Additive exPlanations (SHAP) analysis identified A16, A36, A51, A61, and A22 as the key embedding dimensions, while Pearson correlation analysis further revealed associations between the key dimensions and spectral, radar, texture, and topographic information. Change analysis indicated concurrent urban expansion, ecological recovery, and agricultural land contraction from 2017 to 2024, with cropland-to-forest and cropland-to-impervious-surface conversions being the most prominent transition pathways. Overall, Satellite Embeddings provide effective feature representations for annual land cover mapping and long-term change analysis in complex metropolitan environments.","准确的城市土地覆盖信息对于监测城市化与环境变化至关重要，然而城市景观的复杂性和异质性仍对高分辨率遥感分类构成挑战。本研究以Google Satellite Embeddings作为核心特征输入，结合集成学习与时空后处理，生成了2017—2024年中国37个大城市逐年10 m土地覆盖图。结果表明，加权集成模型的总体精度（OA）达到81.99%，比最优单一模型高2.13%，时空后处理进一步将OA提升至82.59%。与FROM-GLC10等现有土地覆盖产品相比，所提产品的OA高出4.20%。消融实验表明，嵌入表征是性能提升的主要来源。在相同集成学习框架下，Satellite Embedding特征的OA比传统遥感特征高6.54%。SHapley Additive exPlanations（SHAP）分析识别出A16、A36、A51、A61和A22为关键嵌入维度，Pearson相关分析进一步揭示了关键维度与光谱、雷达、纹理和地形信息之间的关联。变化分析表明，2017—2024年间城市扩张、生态恢复与农用地收缩同时发生，其中耕地转为林地和耕地转为不透水面的转换路径最为突出。总体而言，Satellite Embeddings为复杂大城市环境中的逐年土地覆盖制图和长期变化分析提供了有效的特征表征。","Remote Sensing","2026-09-20T00:00:00Z",81,{"impact":18,"substance":66,"depth":67,"authority":68,"freshness":17,"relevant":21,"comment":69},22,19,14,"方法新颖、数据规模大且结论可靠，对城市扩张与耕地变化监测有实质参考价值，但属学术论文而非政策或产业事件，适合作为专业精选。",[71],{"name":62,"url":59},[27,30,73,74,75],"土地覆盖","Google卫星嵌入","耕地变化",[77,78],"Google Satellite Embeddings 土地覆盖","中国大都市 10米土地覆盖制图","GoogleSatelliteEmbeddings土地覆盖-3185","10.3390\u002Frs18183233",{"doi":80,"openalex_id":82,"authors":83,"venue":62,"cited_by_count":35,"oa_url":59,"card":105,"direction":48,"ingested_from":51},"W7213897500",[84,87,90,93,95,97,99,102],{"name":85,"orcid":86},"Yu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-1825-1241",{"name":88,"orcid":89},"Han Liu","https:\u002F\u002Forcid.org\u002F0000-0002-9386-2464",{"name":91,"orcid":92},"Li Wang","https:\u002F\u002Forcid.org\u002F0000-0001-5538-4337",{"name":94,"orcid":9},"Lingling Sang",{"name":96,"orcid":9},"Lili Wang",{"name":98,"orcid":9},"Caisheng Zhao",{"name":100,"orcid":101},"Tengyun Hu","https:\u002F\u002Forcid.org\u002F0009-0001-5514-2167",{"name":103,"orcid":104},"Xuecao Li","https:\u002F\u002Forcid.org\u002F0000-0002-6942-0746",{"tldr":106,"method":107,"finding":108,"direction":48,"opportunity":109},"利用谷歌卫星嵌入生成中国37个大都市2017-2024年10米年度土地覆盖图。","谷歌卫星嵌入特征+集成学习+时空后处理，对比FROM-GLC10并做消融与SHA","集成模型总体精度82.59%，嵌入特征比传统特征高6.54%，揭示城市扩张与生态恢复并存。","可探索卫星嵌入在耕地变化监测与农业用地精细分类中的迁移能力及跨城市泛化性。","2026-09-22T23:30:25.425152Z",{"id":112,"title":113,"url":114,"summary":115,"summary_zh":116,"content":9,"source_name":117,"source_url":114,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":35,"score_detail":119,"sources":121,"tags":123,"search_phrases":126,"slug":129,"view_count":35,"doi":130,"paper":131,"created_at":147},3071,"Urban Influence on Landscape Transformation and Land Cover Interactions During Urban Expansion (1972–2022): A Remote Sensing and GIS-Based Analysis","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0038","Abstract This study investigates how urban expansion influences land cover dynamics. An interactive analytical approach was adopted to examine the transitions among built-up areas, cropland, and bare land throughout the urbanization process. Remote Sensing and GIS techniques, in conjunction with the Land Transfer Matrix (LTM), Landscape Metrics (LM), Multi-buffer Ring (MBR) and complementary spatial tools, were employed to analyze the city of Tiaret in Algeria over the past half century (1972-2022). The results reveal a marked intensification of urbanization, with an Overall Average Annual Urban Expansion Rate (OA-AUER) of 28.3 ha and a concurrent rise in bare land driven by cropland conversion under urban pressure. Notably, 89.4 % of cropland loss initially transitions into bare land, while subsequent urban development originates from cropland (52 %) and bare land (47 %). The observed density patterns in peripheral areas confirm the significant influence of the urban core on surrounding land-cover transformations. These findings elucidate the urbanization process while considering the transitional role of bare land and further explain diffusion coalescence process. Further empirical research is recommended to refine theoretical frameworks on landscape transformation dynamics and to inform policies aimed at mitigating bare-land expansion in peri-urban areas, controlling cropland urbanization, and planning sustainable urban growth.","摘要 本研究探讨城市扩张如何影响土地覆盖动态。采用交互式分析方法，考察城市化进程中建成区、耕地和裸地之间的转换。运用遥感与地理信息系统技术，结合土地转移矩阵（LTM）、景观格局指数（LM）、多缓冲环（MBR）及辅助空间工具，对阿尔及利亚提亚雷特市过去半个世纪（1972—2022年）的变化进行了分析。结果表明，城市化显著加剧，年均城市扩张总面积率（OA-AUER）为28.3公顷，同时在城市压力下耕地转换导致裸地同步增加。值得注意的是，89.4%的耕地损失最初转变为裸地，而随后的城市发展来源于耕地（52%）和裸地（47%）。外围区域观测到的密度格局证实了城市核心对周边土地覆盖变化的显著影响。这些发现阐明了城市化过程，同时考虑了裸地的过渡性作用，并进一步解释了扩散聚合过程。建议开展进一步实证研究，以完善景观转型动态的理论框架，并为旨在减缓城郊地区裸地扩张、控制耕地城市化以及规划可持续城市增长的政策提供依据。","Journal of Landscape Ecology","2026-09-19T00:00:00Z",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":120},"该论文聚焦阿尔及利亚Tiaret市城市扩张与土地覆盖变化，属城市地理与遥感应用研究，与三农、农业信息化、智慧农业主题无直接关联，不建议进入每日精选。",[122],{"name":117,"url":114},[124,125,28,30],"耕地保护","遥感监测",[127,128],"Tiaret 城市扩张 遥感","耕地 建设用地 转移矩阵","Tiaret城市扩张遥感-3071","10.2478\u002Fjlecol-2026-0038",{"doi":130,"openalex_id":132,"authors":133,"venue":117,"cited_by_count":35,"oa_url":141,"card":142,"direction":48,"ingested_from":51},"W7213652939",[134,136,138],{"name":135,"orcid":9},"Amina Kalbaza",{"name":137,"orcid":9},"Belkacem Marir",{"name":139,"orcid":140},"Farida Naceur","https:\u002F\u002Forcid.org\u002F0009-0004-1962-4937","https:\u002F\u002Freference-global.com\u002Fdownload\u002Farticle\u002F10.2478\u002Fjlecol-2026-0038.pdf",{"tldr":143,"method":144,"finding":145,"direction":48,"opportunity":146},"基于遥感与GIS分析阿尔及利亚提亚雷特1972—2022年城市扩张对景观与土地覆盖的影响。","遥感、GIS、土地转移矩阵、景观格局指数与多缓冲环分析。","耕地先转为裸地再被城市占用，城市核心显著驱动外围土地覆盖转变。","可延伸研究裸地作为耕地—城市转换中间态的机制，并构建城郊耕地流失预警模型。","2026-09-21T23:30:24.332177Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":153,"content":9,"source_name":154,"source_url":151,"published_at":11,"category":12,"cover_url":9,"hotness":155,"is_selected":14,"score":156,"score_detail":157,"sources":162,"tags":166,"search_phrases":170,"slug":173,"view_count":35,"doi":174,"paper":175,"created_at":193},3068,"MAPPING LAND USE OF BENUE STATE UNIVERSITY, MAKURDI MAIN CAMPUS, BENUE STATE, NIGERIA USING GEOSPATIAL TECHNIQUES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22865603","This study examined the spatial distribution and extent of land use types within Benue State University (BSU), Makurdi Main Campus, using Geographic Information System (GIS) and remote sensing techniques. Satellite imagery obtained from the National Space Research and Development Agency (NASRDA) was processed and analyzed in ArcGIS 10.8. Land use classification was achieved through on-screen digitization supported by intensive field survey and ground-truthing. The results reveal a heterogeneous land use structure with a total mapped area of 166.69 hectares. Agricultural land use dominates with 20.35%, followed by residential (15.24%) and forest cover (12.29%). Environmentally sensitive land uses, including forestry, green areas, and marshlands, collectively account for 29.18% of the total mapped area, indicating significant ecological value. Institutional land uses (administrative, educational, and religious) account for 15.50%, while commercial and mixed uses constitute 9.29%. The findings highlight a semi-urban institutional landscape undergoing gradual transformation, with increasing development pressures. The study demonstrates the effectiveness of GIS in land use planning and recommends sustainable land management strategies to balance development with environmental conservation.","本研究利用地理信息系统（GIS）和遥感技术，考察了马库尔迪贝努埃州立大学（BSU）主校区内土地利用类型的空间分布与范围。研究对来自国家空间研究与发展局（NASRDA）的卫星影像在ArcGIS 10.8中进行了处理与分析。土地利用分类通过屏幕数字化完成，并辅以密集的实地调查与地面验证。结果显示，该区域土地利用结构具有异质性，总制图面积为166.69公顷。农业用地占主导地位，为20.35%，其次为住宅用地（15.24%）和林地覆盖（12.29%）。环境敏感型土地利用类型，包括林业用地、绿地和沼泽地，合计占总制图面积的29.18%，表明其具有显著的生态价值。机构用地（行政、教育和宗教）占15.50%，而商业和混合用途占9.29%。研究结果凸显出一个正处于逐步转型中的半城市机构景观，其面临日益增大的开发压力。本研究证明了GIS在土地利用规划中的有效性，并建议采取可持续土地管理策略，以平衡开发与环境保护。","Zenodo (CERN European Organization for Nuclear Research)",25,56,{"impact":54,"substance":158,"depth":20,"authority":159,"freshness":160,"relevant":21,"comment":161},16,12,9,"基于GIS与遥感的校园土地利用分类研究，方法规范、数据具体，但属尼日利亚个案，对国内三农与农业信息化参考价值有限。",[163,164],{"name":154,"url":151},{"name":154,"url":165},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22865602",[27,28,167,168,169],"GIS","土地分类","校园规划",[171,172],"土地分类 土地利用 校园规划 遥感","土地分类 土地利用","土地分类土地利用校园规划遥感-3068","10.5281\u002Fzenodo.22865603",{"doi":174,"openalex_id":176,"authors":177,"venue":154,"cited_by_count":35,"oa_url":151,"card":188,"direction":48,"ingested_from":51},"W7213752705",[178,180,182,184,186],{"name":179,"orcid":9},"Alex S. Ortese",{"name":181,"orcid":9},"Innocent E. Bello",{"name":183,"orcid":9},"I. K. SAMAILA",{"name":185,"orcid":9},"M. ALKALI",{"name":187,"orcid":9},"A. Mahmud",{"tldr":189,"method":190,"finding":191,"direction":48,"opportunity":192},"用GIS和遥感技术绘制尼日利亚贝努埃州立大学主校区土地利用分布图。","NASRDA卫星影像，ArcGIS 10.8屏幕数字化，结合实地调查与地面验证。","农业用地占20.35%居首，环境敏感用地共占29.18%，校园呈半城市化转型压力。","可延伸至校园及城郊农业用地动态监测与生态敏感区保护规划研究。","2026-09-21T23:30:23.914355Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":206,"tags":208,"search_phrases":212,"slug":215,"view_count":35,"doi":216,"paper":217,"created_at":235},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems",70,{"impact":159,"substance":203,"depth":204,"authority":20,"freshness":17,"relevant":21,"comment":205},20,17,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[207],{"name":200,"url":197},[209,210,211,27,28],"智慧农业","农业遥感","农业信息化",[213,214],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":216,"openalex_id":218,"authors":219,"venue":200,"cited_by_count":35,"oa_url":228,"card":229,"direction":234,"ingested_from":51},"W7213644047",[220,222,224,226],{"name":221,"orcid":9},"Sreerama Naik Naik S R",{"name":223,"orcid":9},"T K Prasad",{"name":225,"orcid":9},"Feba Jose Jasmine",{"name":227,"orcid":9},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":230,"method":231,"finding":232,"direction":48,"opportunity":233},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.448414Z",{"id":237,"title":238,"url":239,"summary":240,"summary_zh":241,"content":9,"source_name":117,"source_url":239,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":35,"score_detail":242,"sources":244,"tags":246,"search_phrases":248,"slug":251,"view_count":35,"doi":252,"paper":253,"created_at":264},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。",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":243},"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。",[245],{"name":117,"url":239},[247,125,28,30],"深度学习",[249,250],"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":252,"openalex_id":254,"authors":255,"venue":117,"cited_by_count":35,"oa_url":239,"card":259,"direction":48,"ingested_from":51},"W7213670913",[256],{"name":257,"orcid":258},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":260,"method":261,"finding":262,"direction":48,"opportunity":263},"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":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":9,"source_name":271,"source_url":268,"published_at":272,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":273,"sources":275,"tags":277,"search_phrases":281,"slug":284,"view_count":35,"doi":285,"paper":286,"created_at":331},2797,"Generation of representative datasets of future Copernicus Sentinel Expansion Mission Data (hyperspectral, thermal and L-band) as basis for innovative agricultural products","https:\u002F\u002Fdoi.org\u002F10.62880\u002Frars26005","The Copernicus Sentinel Expansion Missions will provide new and unique remote sensing data. To enable rapid use of real data as soon as it becomes available, it is essential to generate comparable synthetic data in advance. This study proposes a novel data set for three of the upcoming sensors. CHIME hyperspectral data are generated by inverting multispectral reflectance data from Sentinel-2 time series by radiative transfer modelling to retrieve land surface parameters and subsequently forward-simulating bottom-of-atmosphere reflectance using expected CHIME sensor characteristics. Future LSTM land surface temperature data are derived from Sentinel-3 and Sentinel-2 data using the Sen-ET workflow with spatial data mining sharpening. L-band backscatter and coherence data for ROSE-L are simulated using SAOCOM-1 data, which are transformed to match the expected spatial and radiometric characteristics. The novel data set is available for three areas of interest (AOIs) defined by Sentinel-2 tiles located in Germany, Belgium, and Estonia. A validation of simulated CHIME data using existing comparable sensor data from EnMAP showed a high spectral correlation with an average RMSE of 6.154 [%] and a correlation of 0.924 for the German AOI in 2024. This publicly available, unique and well validated dataset already enables the preparation and development of future products and services across a wide range of application areas based on data from the Sentinel Expansion Mission. Due to the high data availability resulting from extensive two-year time series, as well as the various AOIs, future products can already be tested for their temporal and spatial transferability.","哥白尼哨兵扩展任务将提供新的独特遥感数据。为了在真实数据可用时尽快加以利用，必须提前生成可比的合成数据。本研究为其中三个即将发射的传感器提出了一个新的数据集。CHIME高光谱数据通过辐射传输建模对来自Sentinel-2时间序列的多光谱反射率数据进行反演，以获取地表参数，随后利用预期的CHIME传感器特征正向模拟大气底层反射率来生成。未来的LSTM地表温度数据利用Sen-ET工作流结合空间数据挖掘锐化方法，从Sentinel-3和Sentinel-2数据中导出。ROSE-L的L波段后向散射和相干性数据使用SAOCOM-1数据进行模拟，并将其转换为符合预期空间和辐射特征的形式。该新数据集可用于三个感兴趣区域（AOIs），分别位于德国、比利时和爱沙尼亚的Sentinel-2瓦片范围内。利用EnMAP现有可比传感器数据对模拟CHIME数据进行的验证表明，2024年德国AOI的光谱相关性较高，平均RMSE为6.154 [%]，相关系数为0.924。这一公开可用、独特且经过充分验证的数据集，已经能够支持基于哨兵扩展任务数据在广泛的应用领域中准备和开发未来产品与服务。由于两年广泛时间序列所带来的高数据可用性以及多个AOIs，未来产品已经可以测试其时间和空间可迁移性。","Recent advances in remote sensing.","2026-09-16T00:00:00Z",{"impact":18,"substance":66,"depth":67,"authority":20,"freshness":160,"relevant":21,"comment":274},"面向未来Sentinel扩展任务的高光谱、热红外与L波段合成数据集研究，方法新颖、验证充分且公开可用，对农业遥感产品预研具有实质价值，值得进入每日精选。",[276],{"name":271,"url":268},[210,278,27,29,279,280],"高光谱","哥白尼计划","合成数据",[282,283],"哥白尼计划 农业遥感 合成数据 地表温度","哥白尼计划 农业遥感","哥白尼计划农业遥感合成数据地表温度-2797","10.62880\u002Frars26005",{"doi":285,"openalex_id":287,"authors":288,"venue":271,"cited_by_count":35,"oa_url":268,"card":326,"direction":48,"ingested_from":51},"W7213413531",[289,291,293,296,299,302,305,308,310,313,315,317,319,321,324],{"name":290,"orcid":9},"Christian Miesgang",{"name":292,"orcid":9},"Sandra Dotzler",{"name":294,"orcid":295},"Anusha Sanmathi Sathyaniranjan","https:\u002F\u002Forcid.org\u002F0009-0009-8710-4622",{"name":297,"orcid":298},"Silke Migdall","https:\u002F\u002Forcid.org\u002F0000-0001-9089-6274",{"name":300,"orcid":301},"Heike Bach","https:\u002F\u002Forcid.org\u002F0000-0001-8060-2498",{"name":303,"orcid":304},"J. A. D. L. Blommaert","https:\u002F\u002Forcid.org\u002F0000-0002-5797-2439",{"name":306,"orcid":307},"Astrid Vannoppen","https:\u002F\u002Forcid.org\u002F0000-0001-5140-832X",{"name":309,"orcid":9},"Louis Snyders",{"name":311,"orcid":312},"Mihkel Veske","https:\u002F\u002Forcid.org\u002F0000-0003-2367-9215",{"name":314,"orcid":9},"Sven Kautlenbach",{"name":316,"orcid":9},"Catherine Odera",{"name":318,"orcid":9},"Tetiana Shtym",{"name":320,"orcid":9},"Tanel Tamm",{"name":322,"orcid":323},"Anke Schickling","https:\u002F\u002Forcid.org\u002F0000-0001-7446-7752",{"name":325,"orcid":9},"Melisa Soledad Heredia",{"tldr":327,"method":328,"finding":329,"direction":48,"opportunity":330},"生成CHIME高光谱、LSTM热红外和ROSE-L L波段模拟数据集，为未来Sentinel扩展任务","辐射传输模型反演、Sen-ET时空锐化、SAOCOM-1模拟，覆盖德比爱三区两年","模拟CHIME与EnMAP光谱相关性0.924，RMSE 6.154%，数据集公开且验证良好。","可基于该模拟数据集提前开发高光谱、热红外与L波段融合的作物监测和表型反演新算法。","2026-09-17T23:30:37.404847Z"]