Invariant Features for Global Crop Type Classification
Laboratory for Information and Decision Systems, MIT（信息与决策系统实验室，麻省理工学院）；Department of Mechanical Engineering, MIT（机械工程系，麻省理工学院）；Institute for Data, Systems, and Society, MIT（数据、系统与社会研究所，麻省理工学院）
Accurate global crop type mapping supports agricultural monitoring and food security, yet remains limited by the scarcity of labeled data in many regions. A key challenge is enabling models trained in one geography to generalize reliably to others despite shifts in climate, phenology, and spectral characteristics. In this work, we show that geographic transfer in crop classification is primarily governed by the ability to learn invariant structure in multispectral time series. To systematically study this, we introduce CropGlobe, a globally distributed benchmark dataset of 300,000 samples spanning eight countries and five continents, and define progressively harder transfer settings from cross-country to cross-hemisphere. Across all settings, we find that simple spectral-temporal representations outperform both handcrafted features and modern geospatial foundation model embeddings. We propose CropNet, a lightweight convolutional architecture that jointly convolves across spectral and temporal dimensions to learn invariant crop signatures. Despite its simplicity, CropNet consistently outperforms larger transformer-based and foundation-model approaches under geographic domain shift. To further improve robustness to geographic variation, we introduce augmentations that simulate shifts in crop phenology and reflectance. Combined with CropNet, this yields substantial gains under large domain shifts. Our results demonstrate that inductive bias toward joint spectral-temporal structure is more critical for transfer than model scale or pretraining, pointing toward a scalable and data-efficient paradigm for worldwide agricultural mapping. Data and code are available at https://github.com/x-ytong/CropNet/.
Fine-grained Hierarchical Crop Type Classification from Integrated Hyperspectral EnMAP Data and Multispectral Sentinel-2 Time Series: A Large-scale Dataset and Dual-stream Transformer Method
Jockey Club STEM Lab of Quantitative Remote Sensing, Department of Geography, The University of Hong Kong, Hong Kong, China（香港大学地理系定量遥感实验室）；Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University, Beijing, China（北航航天智能科学与技术系）
BreizhCrops: A Time Series Dataset for Crop Type Mapping
Technical University of Munich（慕尼黑工业大学）；Univ. Bretagne Sud（南布列塔尼大学）；IRISA（法国国家信息与自动化研究所/雷恩信息与系统研究所（注：IRISA常见中文名为雷恩信息与自动化研究所，为法国国家科研中心与多校共建的独立研究机构，此处保留原名并给出常用译名））
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。
Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin
University of Minnesota（明尼苏达大学）；TechnoServe Labs（TechnoServe实验室）；McKinsey & Company（麦肯锡公司）；University of Pittsburgh（匹兹堡大学）；University of California, Davis（加州大学戴维斯分校）
Mind the Gap: Bridging Prior Shift in Realistic Few-Shot Crop-Type Classification
Technical University of Munich (TUM)（慕尼黑技术大学）；TUM School of Engineering and Design（慕尼黑技术大学工程与设计学院）；Department of Aerospace and Geodesy（航空航天与大地测量系）；Chair of Remote Sensing Technology（遥感技术教授团）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；ELLIS Unit Jena（耶拿ELLIS单元）；University of Jena（耶拿大学）
Bayesian aggregation improves traditional single image crop classification approaches
Center for Computational and Data-Intensive Science and Engineering (CDISE)（计算与数据密集型科学与工程中心（CDISE））；Skolkovo Institute of Science and Technology (Skoltech)（斯科尔科沃科学技术学院（Skoltech））
Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset
Technical University of Munich (TUM)（慕尼黑技术大学）；TUM School of Engineering and Design（慕尼黑技术大学工程与设计学院）；Department of Aerospace and Geodesy（航空航天与大地测量系）；Chair of Remote Sensing Technology（遥感技术教授团）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；ELLIS Unit Jena（耶纳ELLIS单元）；University of Jena（耶纳大学）；ETH Zurich（苏黎世联邦理工学院）；Department of Mathematics（数学系）；Zuse Institute Berlin（柏林Zuse研究所）
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Department of Computer Science, University of Kaiserslautern-Landau (RPTU)（科斯拉特兰大学计算机科学系）；SDS, German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Crop Disease Classification using Support Vector Machines with Green Chromatic Coordinate (GCC) and Attention based feature extraction for IoT based Smart Agricultural Applications
Birla Institute of Technology, Mesra（贝拉理工大学梅斯拉分校）；Maharaja Surajmal Institute of Technology（马哈拉贾·苏拉杰马尔理工学院）；Birla Institute of Technology (BIT), Mesra（贝拉理工大学（BIT）梅斯拉分校）
Low-Resource Crop Classification from Multi-Spectral Time Series Using Lossless Compressors
College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）；College of Science, China Agriculture University（中国农业大学理学院）
Multi-Year Vector Dynamic Time Warping Based Crop Mapping
Scientific and Technological Research Council of Turkey (TÜBİTAK) Space Technologies Research Institute (UZAY)（土耳其科学技术研究理事会（TÜBİTAK）空间技术研究所）；Informatics Institute, Middle East Technical University（中东技术大学信息学研究所）
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。
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）
Transferability and operational reliability of a Prithvi crop classification foundation model under phenological and geographic shift across three continents
University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；NASA Marshall Space Flight Center（NASA马歇尔太空飞行中心）
A Survey of Deep Learning Techniques for Weed Detection from Images
Murdoch University（莫道克大学）；Centre for Crop and Food Innovation（作物与食品创新中心）；Food Futures Institute（食品未来研究所）；Department of Primary Industries and Regional Development, Western Australia（西澳大利亚州初级产业与区域发展部）；Centre for Sustainable Farming Systems（可持续农业系统中心）；Centre of Biosecurity and One Health, Harry Butler Institute（哈里·巴特勒研究所生物安全与同一健康中心）
From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping
College of Forest Resources and Environmental Science, Michigan Technological University（密歇根理工大学森林资源与环境科学学院）；Warnell School of Forestry and Natural Resources, University of Georgia（佐治亚大学沃内尔林业与自然资源学院）；Annis Water Resources Institute, Grand Valley State University（大峡谷州立大学安尼斯水资源研究所）；BioSense Institute（生物感知研究所）；Department of Agricultural, Food, and Resource Economics, Michigan State University（密歇根州立大学农业、食品与资源经济学系）
