[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3618":3,"related-3618":52},{"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":51},3618,"Multi-Target Remote Sensing Segmentation with Cross-Scale Attention for Urban-Ecological Intelligence","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.12.no1.2026.pg131.154","Remote sensing image segmentation is essential to extract valuable information from satellite and aerial images to achieve significant applications such as urban planning and ecological monitoring. Yet, it is hard to accurately segment diverse and complicated features because of the constraints of conventional approaches and the requirement to process variable object sizes and complicated boundaries. This research aims to address the above difficulties through the use and assessment of advanced deep learning models specifically UNet, SegNet, and TransU-Net in multi target semantic segmentation. These networks have been applied in single-object segmentation tasks with a focus on structures and roads, while the UNet architecture was additionally utilized for multi-object segmentation comprising buildings, woods, grasslands, water bodies, and cultivated land. Using appropriate remote sensing datasets, the accuracy of the models was thoroughly evaluated using common evaluation metrics, including pixel accuracy, Intersection over Union (IoU), and F1-score. The experimental outcomes illustrate the capabilities of these deep learning methods to provide accurate identification of essential features. Notably, the maximum attainable accuracy for single-object building segmentation was 96.85%, whereas the overall accuracy for multi-object segmentation with the UNet was 84.2%. The experimental results show these deep learning algorithms can effectively separate different targets from remote sensing imagery, thus making them fundamental tools for geospatial analysis and related fields.","遥感图像分割对于从卫星和航空图像中提取有价值信息，以实现城市规划和生态监测等重要应用至关重要。然而，由于传统方法的局限以及处理可变目标尺寸和复杂边界的需要，准确分割多样且复杂的地物十分困难。本研究旨在通过使用和评估先进的深度学习模型，特别是UNet、SegNet和TransU-Net在多目标语义分割中的应用，来解决上述难题。这些网络已被应用于单目标分割任务，重点关注建筑物和道路，而UNet架构还被额外用于多目标分割，包括建筑物、林地、草地、水体和耕地。利用适当的遥感数据集，通过常用评价指标对模型的精度进行了全面评估，包括像素精度、交并比（IoU）和F1分数。实验结果表明了这些深度学习方法在准确识别关键地物方面的能力。值得注意的是，单目标建筑物分割的最高可达精度为96.85%，而使用UNet进行多目标分割的总体精度为84.2%。实验结果表明，这些深度学习算法能够有效地从遥感图像中分离出不同目标，从而使其成为地理空间分析及相关领域的基础工具。",null,"IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-25T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,18,15,8,1,"将UNet、SegNet、TransU-Net用于多目标遥感语义分割，建筑单目标精度96.85%、多目标84.2%，方法常规但结论对农业与生态遥感监测有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","深度学习","语义分割","遥感","生态监测",[32,33],"UNet 遥感 多目标分割","TransU-Net 城市生态 遥感","UNet遥感多目标分割-3618",0,"10.56201\u002Fijgem.vol.12.no1.2026.pg131.154",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":42,"card":43,"direction":49,"ingested_from":50},"W7214298981",[40],{"name":41,"orcid":9},"Chibueze Favour Aririguzo","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 12 NO. 1 2026\u002FMulti-Target Remote Sensing Segmentation 131-154.pdf",{"tldr":44,"method":45,"finding":46,"direction":47,"opportunity":48},"评估UNet、SegNet和TransU-Net在多目标遥感语义分割中的性能，用于城市与生态监测。","使用UNet、SegNet、TransU-Net模型，在遥感数据集上以像素精度、","单目标建筑分割最高精度96.85%，UNet多目标分割总体精度84.2%。","农业遥感与作物表型","可探索跨尺度注意力机制提升多目标分割精度，并迁移至耕地与作物精细分类。","智慧农业 \u002F 农业物联网","openalex","2026-09-27T23:30:28.366758Z",{"total":53,"page":21,"page_size":53,"items":54},6,[55,99,143,186,233,268],{"id":56,"title":57,"url":58,"summary":59,"summary_zh":60,"content":9,"source_name":61,"source_url":58,"published_at":62,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":63,"score_detail":64,"sources":68,"tags":70,"search_phrases":73,"slug":76,"view_count":35,"doi":77,"paper":78,"created_at":98},3479,"Artificial Intelligence-Enabled Mangrove Ecosystem Monitoring Using Remote Sensing and Environmental Data","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fair.v1i2.252","Mangrove ecosystems play a critical role in coastal protection, carbon sequestration, biodiversity conservation, and climate change mitigation; however, increasing anthropogenic pressures and environmental changes have accelerated mangrove degradation, creating an urgent need for efficient and scalable monitoring approaches. This study aims to develop an Artificial Intelligence-Enabled framework for monitoring mangrove ecosystem conditions by integrating remote sensing imagery with environmental datasets to improve the accuracy and timeliness of ecosystem assessment in tropical coastal regions. The proposed method combines multispectral satellite images, including vegetation indices derived from remote sensing data, with environmental variables such as temperature, precipitation, salinity, and tidal information, which are subsequently processed using a deep learning-based classification model to identify and categorize mangrove health conditions. Experimental evaluation demonstrates that the integration of remote sensing and environmental data significantly enhances model performance compared with approaches relying solely on satellite imagery, achieving high classification accuracy and improving the detection of early signs of ecosystem degradation. The findings further reveal that environmental parameters contribute substantially to distinguishing healthy, moderately degraded, and severely degraded mangrove areas across heterogeneous coastal environments. Consequently, the proposed framework provides an intelligent and reliable decision support tool for environmental monitoring agencies and policymakers while contributing to the development of resilient coastal ecosystem management and sustainable environmental governance in tropical archipelagic regions.","红树林生态系统在海岸防护、碳固存、生物多样性保护及气候变化减缓中发挥着关键作用；然而，日益加剧的人为压力和环境变化加速了红树林退化，亟需高效且可扩展的监测方法。本研究旨在开发一个人工智能驱动的框架，通过整合遥感影像与环境数据集来监测红树林生态系统状况，以提高热带沿海地区生态系统评估的准确性和时效性。所提出的方法将多光谱卫星影像（包括由遥感数据衍生的植被指数）与温度、降水、盐度和潮汐信息等环境变量相结合，随后使用基于深度学习的分类模型进行处理，以识别和分类红树林健康状况。实验评估表明，与仅依赖卫星影像的方法相比，遥感与环境数据的整合显著提升了模型性能，实现了较高的分类精度，并改善了对生态系统退化早期迹象的检测。研究结果进一步揭示，环境参数对于区分异质性沿海环境中健康、中度退化和严重退化的红树林区域具有重要贡献。因此，所提出的框架为环境监测机构和政策制定者提供了一种智能且可靠的决策支持工具，同时有助于热带群岛地区韧性沿海生态系统管理和可持续环境治理的发展。","AI Innovation and Resilience for the Environment (AIR)","2026-09-22T00:00:00Z",76,{"impact":18,"substance":65,"depth":66,"authority":17,"freshness":20,"relevant":21,"comment":67},21,17,"将遥感与环境数据融合的深度学习框架用于红树林健康监测，方法有创新且结论可靠，对沿海生态治理有参考价值，但属细分领域研究，公共影响有限。",[69],{"name":61,"url":58},[26,27,71,30,72],"遥感监测","红树林",[74,75],"红树林 遥感 人工智能","红树林生态系统 监测","红树林遥感人工智能-3479","10.68012\u002Fair.v1i2.252",{"doi":77,"openalex_id":79,"authors":80,"venue":61,"cited_by_count":35,"oa_url":92,"card":93,"direction":49,"ingested_from":50},"W7213895268",[81,84,87,89],{"name":82,"orcid":83},"Dirvi Surya Abbas","https:\u002F\u002Forcid.org\u002F0000-0002-7819-2837",{"name":85,"orcid":86},"Asep Sutarman","https:\u002F\u002Forcid.org\u002F0009-0002-1029-7963",{"name":88,"orcid":9},"Ryan Davis",{"name":90,"orcid":91},"Maulana Abbas","https:\u002F\u002Forcid.org\u002F0009-0009-0137-9650","https:\u002F\u002Fjournal.sundarapublishing.com\u002Findex.php\u002Fair\u002Farticle\u002Fdownload\u002F252\u002F142",{"tldr":94,"method":95,"finding":96,"direction":47,"opportunity":97},"融合遥感影像与环境数据，用深度学习构建红树林生态系统健康监测框架。","多光谱卫星影像与植被指数，结合温度、降水、盐度、潮汐等环境变量，训练深度学习分类","融合环境数据显著提升分类精度，能更早识别红树林退化迹象，有效区分不同退化程度。","可探索多源时序遥感与环境数据融合的早期退化预警，并迁移至其他滨海湿地生态系统监测。","2026-09-25T23:30:14.373467Z",{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":62,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":112,"tags":114,"search_phrases":117,"slug":120,"view_count":35,"doi":121,"paper":122,"created_at":142},3277,"Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193272","Soil moisture (SM) is critical for climate, water, and agriculture, but Soil Moisture Active Passive (SMAP) passive microwave products have coarse resolution, limiting regional applications. This study develops an SM downscaling framework based on Transformer and its variants (PatchTST and iTransformer), integrating multi-source satellite and groundwater data to generate 1 km daily SM products (2015–2022). Compared with Random Forest (RF), Long Short-Term Memory (LSTM), and Convolutional Neural Network–LSTM (CNN-LSTM), Transformer and its variants achieve superior accuracy and generalization. Validated against in situ measurements and SMCI1.0, the Transformer-downscaled SM product achieved the best accuracy with ubRMSE = 0.0372 m3\u002Fm3 and RMSE = 0.0591 m3\u002Fm3. The downscaled SM dataset not only captured finer spatial details but also preserved the spatial patterns and seasonal dynamics of the original SMAP product and showed good responsiveness to precipitation events. Feature importance analysis revealed that, aside from precipitation, the diurnal land surface temperature difference had a greater impact on SM than individual daytime or nighttime land surface temperature, ranking just below vegetation indices and soil texture factors, while groundwater level showed higher importance than elevation and surface temperature. This study confirms the effectiveness of Transformer-based models for SM spatial downscaling, providing a novel framework integrating remote sensing and deep hydrological information to generate accurate 1 km SM products.","土壤水分（SM）对气候、水资源和农业至关重要，但土壤水分主动被动（SMAP）被动微波产品分辨率较粗，限制了区域应用。本研究构建了一个基于Transformer及其变体（PatchTST和iTransformer）的土壤水分降尺度框架，融合多源卫星和地下水数据，生成1 km日尺度土壤水分产品（2015—2022年）。与随机森林（RF）、长短期记忆网络（LSTM）和卷积神经网络—长短期记忆网络（CNN-LSTM）相比，Transformer及其变体取得了更高的精度和泛化能力。利用站点实测数据和SMCI1.0进行验证，Transformer降尺度土壤水分产品精度最优，ubRMSE = 0.0372 m³\u002Fm³，RMSE = 0.0591 m³\u002Fm³。降尺度土壤水分数据集不仅捕捉到了更精细的空间细节，还保留了原始SMAP产品的空间格局和季节动态，并对降水事件表现出良好的响应。特征重要性分析表明，除降水外，昼夜地表温差对土壤水分的影响大于单独的白天或夜间地表温度，其重要性仅次于植被指数和土壤质地因子，而地下水埋深的重要性高于高程和地表温度。本研究证实了基于Transformer的模型在土壤水分空间降尺度中的有效性，为融合遥感和深层水文信息生成准确的1 km土壤水分产品提供了一种新框架。","Remote Sensing",81,{"impact":18,"substance":108,"depth":18,"authority":109,"freshness":110,"relevant":21,"comment":111},22,14,9,"基于Transformer的SMAP土壤水分1km降尺度研究，方法新颖、验证充分，对区域农业旱情监测有实用价值。",[113],{"name":105,"url":102},[115,26,27,29,116],"智慧农业","土壤墒情",[118,119],"SMAP 土壤水分 降尺度","Transformer 土壤水分 安徽","SMAP土壤水分降尺度-3277","10.3390\u002Frs18193272",{"doi":121,"openalex_id":123,"authors":124,"venue":105,"cited_by_count":35,"oa_url":102,"card":137,"direction":47,"ingested_from":50},"W7208807695",[125,127,129,131,133,135],{"name":126,"orcid":9},"Yuyang Fan",{"name":128,"orcid":9},"Jianwei Ma",{"name":130,"orcid":9},"Mengmeng Li",{"name":132,"orcid":9},"Changqing Ke",{"name":134,"orcid":9},"Bin Cheng",{"name":136,"orcid":9},"Zheng Duan",{"tldr":138,"method":139,"finding":140,"direction":47,"opportunity":141},"基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。","Transformer、PatchTST、iTransformer，融合多源卫星","Transformer降尺度产品精度最优（ubRMSE=0.0372），保留原产品时空格局并响应降水","可探索Transformer降尺度产品在区域干旱监测、灌溉决策及作物估产中的耦合应用。","2026-09-23T23:30:19.132307Z",{"id":144,"title":145,"url":146,"summary":147,"summary_zh":148,"content":9,"source_name":149,"source_url":146,"published_at":150,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":151,"score_detail":152,"sources":155,"tags":157,"search_phrases":160,"slug":163,"view_count":35,"doi":164,"paper":165,"created_at":185},2942,"Deep learning-driven multisource remote sensing image fusion: Advances, challenges, and future directions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116319","Multisource remote sensing image fusion has become an important solution to a long-standing limitation in Earth observation: individual sensors rarely provide high spatial detail, rich spectral information, reliable structural sensitivity, and frequent temporal coverage at the same time. This review examines how deep learning and artificial intelligence are being used to integrate multispectral, hyperspectral, panchromatic, optical, and synthetic aperture radar imagery for more reliable interpretation of complex ground scenes. It provides a technical synthesis of convolutional neural networks, autoencoders, generative adversarial networks, transformer architectures, diffusion models, and hybrid model driven approaches, with attention to their fusion mechanisms, reconstruction behavior, computational demand, and suitability for operational use. Applications include land cover mapping, precision agriculture, environmental monitoring, urban analysis, disaster assessment, and defense related interpretation. Rather than treating each fusion task separately, this review connects sensor heterogeneity, spatial and spectral resolution trade offs, radiometric correction, geometric correction, registration, noise reduction, and fusion level design within a single framework. The analysis indicates that convolutional models remain effective for stable local detail recovery, adversarial learning can improve visual sharpness but may introduce spectral distortion, transformer models better capture long range spatial and spectral relationships, and diffusion models offer refined reconstruction at greater computational cost. The review further identifies open challenges involving misregistration, spectral bias, limited labeled data, weak generalization across sensors, high memory requirements, and limited interpretability. Future progress should prioritize sensor aware learning, self supervised training, uncertainty aware evaluation, lightweight deployment, and application oriented benchmarks to improve reliability in operational Earth observation.","多源遥感图像融合已成为解决地球观测领域一个长期局限的重要方案：单一传感器很少能够同时提供高空间细节、丰富光谱信息、可靠的结构敏感性以及频繁的时间覆盖。本文综述了如何利用深度学习和人工智能整合多光谱、高光谱、全色、光学和合成孔径雷达（synthetic aperture radar, SAR）影像，以更可靠地解译复杂地表场景。文章对卷积神经网络、自编码器、生成对抗网络、Transformer架构、扩散模型以及混合模型驱动方法进行了技术综合，重点关注其融合机制、重建行为、计算需求以及业务化适用性。应用领域包括土地覆盖制图、精准农业、环境监测、城市分析、灾害评估和国防相关解译。本文并非将每种融合任务分开处理，而是在一个统一框架内将传感器异质性、空间与光谱分辨率权衡、辐射校正、几何校正、配准、降噪和融合层级设计联系起来。分析表明，卷积模型在稳定的局部细节恢复方面仍然有效，对抗学习可以提升视觉锐度但可能引入光谱失真，Transformer模型能更好地捕捉长程空间与光谱关系，而扩散模型以更高的计算成本提供精细重建。本文进一步指出了涉及配准误差、光谱偏差、标注数据有限、跨传感器泛化能力弱、高内存需求以及可解释性有限等开放挑战。未来的进展应优先关注传感器感知学习、自监督训练、不确定性感知评估、轻量化部署以及面向应用的基准测试，以提高业务化地球观测的可靠性。","Engineering Applications of Artificial Intelligence","2026-09-18T00:00:00Z",82,{"impact":18,"substance":108,"depth":153,"authority":109,"freshness":110,"relevant":21,"comment":154},19,"发表于核心期刊的综述，系统梳理深度学习多源遥感融合的方法、应用与挑战，对农业遥感与精准农业有直接参考价值，时效性强，值得进入每日精选。",[156],{"name":149,"url":146},[26,27,158,29,159],"精准农业","多源数据融合",[161,162],"多源遥感 图像融合 深度学习","农业人工智能 多源数据融合 深度学习 精准农业","多源遥感图像融合深度学习-2942","10.1016\u002Fj.engappai.2026.116319",{"doi":164,"openalex_id":166,"authors":167,"venue":149,"cited_by_count":35,"oa_url":146,"card":180,"direction":47,"ingested_from":50},"W7213544281",[168,171,174,177],{"name":169,"orcid":170},"Shahid Karim","https:\u002F\u002Forcid.org\u002F0000-0001-9986-5052",{"name":172,"orcid":173},"Akeel Qadir","https:\u002F\u002Forcid.org\u002F0000-0003-0358-6505",{"name":175,"orcid":176},"Asif Ali Laghari","https:\u002F\u002Forcid.org\u002F0000-0001-5831-5943",{"name":178,"orcid":179},"Irfana Bibi","https:\u002F\u002Forcid.org\u002F0000-0003-2794-504X",{"tldr":181,"method":182,"finding":183,"direction":47,"opportunity":184},"综述深度学习多源遥感图像融合方法、挑战与未来方向。","综述CNN、GAN、Transformer、扩散模型等融合机制与重建行为。","CNN擅局部细节，GAN易谱失真，Transformer长程关系强，扩散模型精度高但算力大。","面向农业的轻量、自监督、不确定性感知融合与基准数据集构建。","2026-09-19T23:30:32.767384Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":194,"sources":196,"tags":198,"search_phrases":201,"slug":204,"view_count":35,"doi":205,"paper":206,"created_at":232},2790,"High-resolution land cover mapping from coarse labels via a noisy label learning-guided cross-scale framework","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2726002","High-resolution remote sensing images (HRSIs) provide essential data support for land cover mapping, where deep learning has shown great promise. However, deep learning-based methods rely on abundant high-quality annotations, while low-resolution coarse labels are difficult to use directly in HRSIs training. In this paper, a novel noisy label learning-guided cross-scale framework (NL-CSF) is proposed, which is designed to achieve high-resolution land cover mapping from coarse labels. First, a spectral-based label mask filtering strategy is developed to preliminarily optimize coarse labels. Then, an adaptive noise evaluation scheme is introduced that assigns loss weights based on the noise differences between image and label patches in the training set. Finally, we design a cross-scale transfer Transformer (CSTT) model based on the vision Transformer (ViT) architecture, and the training process is guided by a noise-weighted loss function. Two cross-scale datasets are utilized to evaluate the performance of NL-CSF in multiple spatial scale differences (10 m to 3 m, 3 m to 0.5 m, and 10 m to 0.5 m). Experimental results demonstrate that NL-CSF improves overall accuracy (OA) by at least 7%, 6%, and 4% across the three cross-scale tasks in the first dataset, and by at least 2%, 9%, and 4% in the second dataset, respectively, compared with existing methods. Furthermore, the proposed framework is applied to cross-scale mapping across Jianye District of Nanjing (urban), Sheyang County of Yancheng (agricultural), and the Yellow River Delta of Dongying (wetland), leveraging a low-resolution land cover product and high-resolution PlanetScope images to generate more precise land cover maps. These results demonstrate the effectiveness of the proposed framework in mitigating the impact of noisy coarse labels and generating reliable high-resolution land cover maps.","高分辨率遥感影像(high-resolution remote sensing images, HRSIs)为土地覆盖制图提供了重要的数据支撑，深度学习在此领域展现出巨大潜力。然而，基于深度学习的方法依赖于大量高质量标注，而低分辨率粗标签难以直接用于高分辨率遥感影像训练。本文提出了一种新的噪声标签学习引导的跨尺度框架(noisy label learning-guided cross-scale framework, NL-CSF)，旨在从粗标签实现高分辨率土地覆盖制图。首先，提出了一种基于光谱的标签掩膜过滤策略，对粗标签进行初步优化。然后，引入了一种自适应噪声评估方案，根据训练集中影像块与标签块之间的噪声差异分配损失权重。最后，基于视觉Transformer(vision Transformer, ViT)架构设计了跨尺度迁移Transformer(cross-scale transfer Transformer, CSTT)模型，并以噪声加权损失函数引导训练过程。利用两个跨尺度数据集评估NL-CSF在多种空间尺度差异(10 m至3 m、3 m至0.5 m、10 m至0.5 m)下的性能。实验结果表明，与现有方法相比，NL-CSF在第一个数据集的三个跨尺度任务中总体精度(overall accuracy, OA)分别至少提升7%、6%和4%，在第二个数据集中分别至少提升2%、9%和4%。此外，将所提框架应用于南京建邺区(城市)、盐城射阳县(农业)和东营黄河三角洲(湿地)的跨尺度制图，利用低分辨率土地覆盖产品和高分PlanetScope影像生成更精确的土地覆盖图。这些结果证明了所提框架在减轻噪声粗标签影响和生成可靠高分辨率土地覆盖图方面的有效性。","GIScience & Remote Sensing","2026-09-16T00:00:00Z",{"impact":18,"substance":108,"depth":18,"authority":109,"freshness":110,"relevant":21,"comment":195},"提出噪声标签学习引导的跨尺度框架，用低分辨率粗标签生成高分辨率土地覆盖图，精度提升显著，对农业遥感监测有实用价值。",[197],{"name":192,"url":189},[26,27,29,199,200],"土地覆盖","高分辨率制图",[202,203],"农业人工智能 高分辨率制图 土地覆盖 深度学习","农业人工智能 高分辨率制图","农业人工智能高分辨率制图土地覆盖深度学习-2790","10.1080\u002F15481603.2026.2726002",{"doi":205,"openalex_id":207,"authors":208,"venue":192,"cited_by_count":35,"oa_url":189,"card":227,"direction":47,"ingested_from":50},"W7213283296",[209,212,215,218,221,223,225],{"name":210,"orcid":211},"Xiangyu Nie","https:\u002F\u002Forcid.org\u002F0009-0001-5095-6401",{"name":213,"orcid":214},"Cong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-5386-7343",{"name":216,"orcid":217},"Wei Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8162-9422",{"name":219,"orcid":220},"Hong Fang","https:\u002F\u002Forcid.org\u002F0000-0003-3707-0910",{"name":222,"orcid":9},"Zhen Dong",{"name":224,"orcid":9},"Sicong Liu",{"name":226,"orcid":9},"Zhaohui Xue",{"tldr":228,"method":229,"finding":230,"direction":47,"opportunity":231},"提出噪声标签学习引导的跨尺度框架，用低分辨率粗标签生成高分辨率土地覆盖图。","谱掩膜过滤粗标签、自适应噪声评估加权损失、基于ViT的跨尺度迁移Transfor","在多个跨尺度任务上总体精度提升2%-9%，并在城市、农业、湿地场景生成更精确土地覆盖图。","可探索将粗标签跨尺度学习用于作物精细分类与长时序农情监测，降低高精度标注依赖。","2026-09-17T23:30:34.952106Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":246,"tags":248,"search_phrases":250,"slug":253,"view_count":21,"doi":254,"paper":255,"created_at":267},1769,"Land Cover Classification using Hyperspectral Imagery: A Deep Learning Approach","https:\u002F\u002Fdoi.org\u002F10.17485\u002Fijst\u002Fv19i31.487","Objectives: This research aimed to perform proper land cover classification of the Krishnagiri and Dharmapuri districts using hyperspectral imagery. This research also attempted to determine the usefulness of deep learning models in detecting large land cover classes, such as quarry, barren, forest, built-up, and agricultural land, using hyperspectral data. Method: The analysis of the land cover patterns in the study area used hyperspectral images (HSI) taken between 2015 and 2022, which had 230 spectral bands. A total of 3000 hyperspectral images were processed, 2000 of which were utilized for training and 1000 for testing. The images were then preprocessed, segmented into superpixels using superpixel segmentation, and classified. Three deep learning models, a Convolutional Neural Network (CNN), Residual Neural Network 18 (ResNet-18), and Visual Geometry Group-16 (VGG-16), were used as classification models. Standard measures of accuracy, precision, recall, and F-score were used to evaluate the performance and effectiveness of the models. Findings: The results indicate that ResNet-18 outperformed the other models in determining land cover classes using hyperspectral images. The model has shown high classification levels among various types of land, with built-up land having an accuracy level of approximately 95.09, indicating a better ability to extract and classify features than CNN and VGG-16. Novelty: The novelty of this study lies in the combination of hyperspectral image processing with deep learning frameworks to classify regions based on land cover. The relative analysis of CNN, ResNet-18, and VGG-16 demonstrated the usefulness of residual learning networks in enhancing the classification accuracy in hyperspectral remote sensing scenarios. Keywords: Hyperspectral Image (HSI), Land Cover Classification, Deep Learning, Convolutional Neural Network (CNN), ResNet-18, VGG-16, Remote Sensing, Spectral–Spatial Analysis","目标：本研究旨在利用高光谱影像对克里希纳吉里区和达尔马普里区进行准确的土地覆盖分类，并尝试确定深度学习模型在利用高光谱数据检测大型土地覆盖类别（如采石场、荒地、森林、建成区和农业用地）方面的有效性。方法：研究区域的土地覆盖模式分析使用了2015年至2022年间获取的高光谱图像（HSI），共包含230个光谱波段。共处理了3000幅高光谱图像，其中2000幅用于训练，1000幅用于测试。图像经过预处理后，采用超像素分割方法将其分割为超像素，并进行分类。使用三种深度学习模型作为分类模型，即卷积神经网络（CNN）、残差神经网络18（ResNet-18）和视觉几何组-16（VGG-16）。采用准确率、精确率、召回率和F值等标准指标来评估模型的性能和有效性。结果：结果表明，ResNet-18在利用高光谱图像确定土地覆盖类别方面优于其他模型。该模型在各种土地类型中表现出较高的分类水平，其中建成区的准确率约为95.09，表明其在特征提取和分类能力上优于CNN和VGG-16。新颖性：本研究的创新之处在于将高光谱图像处理与深度学习框架相结合，基于土地覆盖对区域进行分类。CNN、ResNet-18和VGG-16的相对分析证明了残差学习网络在提高高光谱遥感场景分类准确性方面的有效性。关键词：高光谱图像（HSI）、土地覆盖分类、深度学习、卷积神经网络（CNN）、ResNet-18、VGG-16、遥感、光谱-空间分析","Indian Journal of Science and Technology","2026-09-05T00:00:00Z",58,{"impact":17,"substance":18,"depth":243,"authority":13,"freshness":244,"relevant":21,"comment":245},16,2,"研究比较了三种深度学习模型在高光谱影像土地分类中的表现，方法有参考价值，但时效性差，影响范围有限。",[247],{"name":239,"url":236},[26,27,29,249],"土地分类",[251,252],"农业人工智能 土地分类 深度学习 遥感","农业人工智能 土地分类","农业人工智能土地分类深度学习遥感-1769","10.17485\u002Fijst\u002Fv19i31.487",{"doi":254,"openalex_id":256,"authors":257,"venue":239,"cited_by_count":35,"oa_url":236,"card":262,"direction":47,"ingested_from":50},"W7208825957",[258,260],{"name":259,"orcid":9},"P Nithya",{"name":261,"orcid":9},"P Sudhakar",{"tldr":263,"method":264,"finding":265,"direction":47,"opportunity":266},"利用高光谱影像和深度学习模型对印度两地区进行土地覆盖分类，比较CNN、ResNet-18和VGG-1","使用2015-2022年230波段高光谱影像，超像素分割后，用CNN、ResNe","ResNet-18表现最佳，建成区分类准确率约95.09%，优于CNN和VGG-16。","可探索将ResNet-18等深度学习模型应用于高光谱影像的作物精细分类，或结合多时相数据提升农业监测精度。","2026-09-06T23:30:27.341932Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":9,"source_name":274,"source_url":271,"published_at":275,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":276,"score_detail":277,"sources":281,"tags":283,"search_phrases":285,"slug":288,"view_count":21,"doi":289,"paper":290,"created_at":307},1729,"CAFF-Net for coupled eco-health prediction of urban green space and water body with multi-source remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.asej.2026.104404","Aiming at three bottlenecks of multi-source remote sensing eco-health inversion: cross-modal feature redundancy, decoupled single-task modeling, and detail distortion of fragmented urban land covers, this paper proposes CAFF-Net for coupled eco-health prediction of urban green spaces and water bodies using Sentinel-1 SAR and Sentinel-2 optical data. A dual-branch Swin-Transformer separately extracts heterogeneous features, and serial CBAM realizes adaptive feature filtering to suppress redundant information. An improved multi-task UNet synchronously predicts green space, water body and coupled ecological health indices with joint loss to model aquatic-terrestrial ecological linkages. Experiments show CAFF-Net achieves an 𝑅 2 of 0.928, 3.3% higher than SOTA, with strong robustness under heavy cloud interference, supporting sponge city and watershed governance.","针对多源遥感生态健康反演中存在的三大瓶颈——跨模态特征冗余、单任务解耦建模以及城市破碎化地物细节失真问题，本文提出CAFF-Net，利用Sentinel-1 SAR与Sentinel-2光学数据实现城市绿地和水体耦合生态健康预测。双分支Swin-Transformer分别提取异质特征，串联CBAM实现自适应特征过滤以抑制冗余信息。改进的多任务UNet通过联合损失同步预测绿地、水体及耦合生态健康指数，以建模水陆生态关联。实验表明，CAFF-Net的R²达到0.928，较SOTA提升3.3%，且在强云干扰下具有较强鲁棒性，可为海绵城市和流域治理提供支撑。","Ain Shams Engineering Journal","2026-09-03T00:00:00Z",65,{"impact":17,"substance":278,"depth":18,"authority":17,"freshness":279,"relevant":21,"comment":280},20,3,"提出CAFF-Net模型，耦合预测城市绿地与水体生态健康，方法新颖，数据可靠，对智慧城市生态管理有参考价值。",[282],{"name":274,"url":271},[115,26,29,30,284],"城市绿地",[286,287],"农业人工智能 城市绿地 智慧农业 生态监测","农业人工智能 城市绿地","农业人工智能城市绿地智慧农业生态监测-1729","10.1016\u002Fj.asej.2026.104404",{"doi":289,"openalex_id":291,"authors":292,"venue":274,"cited_by_count":35,"oa_url":301,"card":302,"direction":49,"ingested_from":50},"W7207862972",[293,296,298],{"name":294,"orcid":295},"Yifeng Shen","https:\u002F\u002Forcid.org\u002F0009-0009-3887-4938",{"name":297,"orcid":9},"Y. F. Ye",{"name":299,"orcid":300},"Shulai Wang","https:\u002F\u002Forcid.org\u002F0009-0007-7101-3449","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2090447926004351\u002Fpdf",{"tldr":303,"method":304,"finding":305,"direction":47,"opportunity":306},"提出CAFF-Net，用多源遥感数据耦合预测城市绿地和水体生态健康。","双分支Swin-Transformer提取特征，CBAM过滤冗余，改进多任务UN","R²达0.928，比SOTA高3.3%，抗云干扰强，支持海绵城市治理。","可延伸至农业生态系统，耦合预测农田植被与水体健康，探索多任务联合损失在农业遥感中的应用。","2026-09-05T23:30:13.221496Z"]