本文的目的是通过对卫星图像时间序列进行分类来绘制农业作物图。农业领域的专家使用以分层树结构组织的作物类型标签，其中粗类（如果园）被细分为细类（如苹果，梨，葡萄藤等）。我们开发了一种作物分类方法，利用这种专家知识，并显着提高稀有作物类型的映射。三级标签层次结构被编码在卷积递归神经网络（convRNN）中，使得对于每个像素，模型以不同的粒度级别预测三个标签。这种端到端可训练的分层网络架构允许模型学习稀有类的联合特征表示（例如，苹果，梨）在较粗糙的水平（例如，果园），从而提高细粒度级别的分类性能。此外，以不同粒度进行标记还可以根据分类分数调整输出;因为具有高置信度的粗标签有时比细粒度但非常不确定的标签更适用于农业实践。我们在一个新的，我们公开的大型数据集上验证了所提出的方法。ZueriCrop覆盖了瑞士苏黎世州和图尔高州50 km x 48 km的区域，共有116，000个独立的田地，跨越48个作物类别，以及来自Sentinel-2的28，000个（多时相）图像块。我们将我们提出的分层convRNN模型与几个基线进行了比较，包括为不平衡类分布设计的方法。分层方法在F1评分中至少优于9.9个百分点。
英文摘要：
The aim of this paper is to map agricultural crops by classifying satellite image time series. Domain experts in agriculture work with crop type labels that are organised in a hierarchical tree structure, where coarse classes (like orchards) are subdivided into finer ones (like apples, pears, vines, etc.). We develop a crop classification method that exploits this expert knowledge and significantly improves the mapping of rare crop types. The three-level label hierarchy is encoded in a convolutional, recurrent neural network (convRNN), such that for each pixel the model predicts three labels at different level of granularity. This end-to-end trainable, hierarchical network architecture allows the model to learn joint feature representations of rare classes (e.g., apples, pears) at a coarser level (e.g., orchard), thereby boosting classification performance at the fine-grained level. Additionally, labelling at different granularity also makes it possible to adjust the output according to the classification scores; as coarser labels with high confidence are sometimes more useful for agricultural practice than fine-grained but very uncertain labels. We validate the proposed method on a new, large dataset that we make public. ZueriCrop covers an area of 50 km x 48 km in the Swiss cantons of Zurich and Thurgau with a total of 116'000 individual fields spanning 48 crop classes, and 28,000 (multi-temporal) image patches from Sentinel-2. We compare our proposed hierarchical convRNN model with several baselines, including methods designed for imbalanced class distributions. The hierarchical approach performs superior by at least 9.9 percentage points in F1-score.
Pseudo-Labeling by Multi-Policy Viewfinder Network for Image Cropping
Huazhong University of Science and Technology（华中科技大学）；School of Artificial Intelligence and Automation（人工智能与自动化学院）；Key Laboratory of Image Processing and Intelligent Control, Ministry of Education（图像处理与智能控制教育部重点实验室）
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（亥姆霍兹德累斯顿罗森多夫研究中心（HZDR））；Technical University of Munich (TUM)（慕尼黑工业大学（TUM））；Getulio Vargas Foundation（热图利奥·瓦加斯基金会）；Institute of Advanced Research in Artificial Intelligence (IARAI)（人工智能高级研究所（IARAI））
MT-CYP-Net: Multi-Task Network for Pixel-Level Crop Yield Prediction Under Very Few Samples
State Key Laboratory of Water Resources Engineering and Management, Wuhan University（水利水电工程与管理国家重点实验室，武汉大学）；School of Computer Science, Wuhan University（武汉大学计算机学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）；College of Agricultural Science and Engineering, Hohai University（河海大学农业科学与工程学院）
Improving Data Efficiency for Plant Cover Prediction with Label Interpolation and Monte-Carlo Cropping
Friedrich Schiller University（弗里德里希·席勒大学）；German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig（德国综合生物多样性研究中心（iDiv）哈雷-耶拿-莱比锡）；Michael Stifel Center Jena（耶拿米夏埃尔·施蒂费尔中心）
SPROUT: A Scalable Diffusion Foundation Model for Multi-Crop Plant Phenotyping
Graduate School of Agricultural and Life Sciences, The University of Tokyo（东京大学大学院农学生命科学研究科）；National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学多谱信息智能处理技术全国重点实验室、人工智能与自动化学院）；Institute of Agricultural Machinery, NARO（日本国立研究开发法人农业·食品产业技术综合研究机构农业机械研究所）；Institute of Life and Environmental Sciences, University of Tsukuba（筑波大学生命环境科学研究所）
Weakly Supervised Framework Considering Multi-temporal Information for Large-scale Cropland Mapping with Satellite Imagery
School of Geosciences and Info-Physics, Central South University（中南大学地球科学与信息物理学院）；The 27th Research Institute, China Electronic Technology Group Corporation（中国电子科技集团公司第二十七研究所）
Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach
University of Minnesota Twin Cities（明尼苏达大学双城分校）；Ant Group（蚂蚁集团）；Texas Tech University（德克萨斯理工大学）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与资源研究所）；Stanford University（斯坦福大学）
Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision
Goldman School of Public Policy, UC Berkeley（加州大学伯克利分校高盛公共政策学院）；Institute for Computational and Mathematical Engineering, Stanford University（斯坦福大学计算与数学工程研究所）；Department of Earth System Science, Stanford University（斯坦福大学地球系统科学系）；European Commission Joint Research Centre（欧盟委员会联合研究中心）
Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results
Federal University of São Carlos（圣卡洛斯联邦大学）；John Deere（约翰迪尔）
在精准农业中，检测高产农田是一项必不可少的实践，使农民能够单独评估操作性能，并比较不同的种子品种，农药和肥料。然而，手动识别生产领域往往是一项耗时且容易出错的任务。以前的研究探索了使用先进的机器学习算法检测农田的不同方法，但它们通常缺乏高质量的标记数据。在这种情况下，我们提出了一个高质量的数据集，该数据集由机器操作生成，并与随时间推移跟踪的Sentinel-2图像相结合。据我们所知，这是第一个使用该技术来克服缺乏标记样本的问题。接下来，我们应用未标记数据的半监督分类和最先进的监督和自监督深度学习方法来自动检测高产农田。最后，结果证明了正未标记学习的高准确性，这完全符合我们对正样本具有高置信度的问题。考虑到存在精确的数据集和对比学习（考虑到我们没有全面的标记数据集可用的情况），在Triplet Loss Siamese中发现了最佳性能。
Beyond Labels: A Self-Supervised Framework with Masked Autoencoders and Random Cropping for Breast Cancer Subtype Classification
DISIT, Computer Science Institute, University of Piemonte Orientale（DISIT计算机科学研究所，皮埃蒙特东方大学）；Center for Translational Research on Autoimmune and Allergic Diseases (CAAD), Department of Health Sciences, University of Piemonte Orientale（自身免疫和过敏疾病转化研究中心（CAAD），健康科学系，皮埃蒙特东方大学）；Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Department of Health Sciences, University of Piemonte Orientale（自身免疫疾病跨学科研究中心（IRCAD），健康科学系，皮埃蒙特东方大学）
提出一种基于 MAE 与随机裁剪的自监督框架，从 WSI 学习组织病理图像表示，并通过线性探针完成乳腺癌多亚型分类。
Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers
School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University（数学与统计学学院和教育部智能网络与网络安全重点实验室，西安交通大学）；Tencent AI Lab（腾讯AI实验室）；ByteDance（字节跳动）；SINGPATH AI Lab, SingPath Medical Technology Pte. Ltd.（SingPath AI实验室，SingPath医疗科技私人有限公司）；School of Mathematics and Statistics, Xidian University（数学与统计学学院，西安电子科技大学）；College of Biomedical Engineering, Sichuan University（生物医学工程学院，四川大学）；School of Mathematics and Statistics, Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University（数学与统计学学院和教育部智能网络与网络安全重点实验室，西安交通大学）；Macao Institute of Systems Engineering, Macau University of Science and Technology（澳门系统工程研究院，澳门科技大学）
Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation
The Hong Kong University of Science and Technology（香港科技大学）；Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology Shenzhen Research Institute（香港科技大学深圳研究院）
Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping
Analytics Lab (A-Lab), UM6P, Rabat 11103, Morocco（安利实验室（A-Lab））；Center for Remote Sensing Applications (CRSA), UM6P, Ben Guerir 43150, Morocco（远程传感应用中心（CRSA））；Friedrich Schiller University Jena, Department of Geography, Jena 07743, Germany（耶拿弗里德里希·施勒格尔大学）；Dept. of Cartography（制图部门）
