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））
Marc Rußwurm, Charlotte Pelletier, Maximilian Zollner, Sébastien Lefèvre, Marco Körner
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France. We compare seven recently proposed deep neural networks along with a Random Forest baseline. The dataset, model (re-)implementations and pre-trained model weights are available at the associated GitHub repository (https://github.com/dl4sits/BreizhCrops) that has been designed with applicability for practitioners in mind. We plan to maintain the repository with additional data and welcome contributions of novel methods to build a state-of-the-art benchmark on methods for crop type mapping.
In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. Given a geographical region with fully mapped coffee plantations, we observe that this knowledge can be used to train a classifier and to map a new county with no need of samples indicated in the target region. Experimental results show that transferring knowledge via some UDA strategies performs better than just applying a classifier trained in a region to predict coffee crops in a new one. However…
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）
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Univ. Bretagne Sud（南布列塔尼大学）；CNRS（法国国家科学研究中心）；IRISA（法国信息与随机系统研究所）；Univ Lyon（里昂大学）；UJM-Saint-Etienne（圣艾蒂安大学）；Institut d Optique Graduate School（光学研究所研究生院）；Univ. Rennes（雷恩大学）；LETG（地理环境实验室）
Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to produce up-to-date crop cover maps. Most time series classification approaches focus on the accuracy of predictions. However, the earliness of the prediction is also of great importance since coming to an early decision can make a crucial difference in time-sensitive applications. In this work, we present an End-to-End Learned Early Classifica…
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））
The application of deep learning algorithms to Earth observation (EO) in recent years has enabled substantial progress in fields that rely on remotely sensed data. However, given the data scale in EO, creating large datasets with pixel-level annotations by experts is expensive and highly time-consuming. In this context, priors are seen as an attractive way to alleviate the burden of manual labeling when training deep learning methods for EO. For some applications, those priors are readily available. Motivated by the great success of contrastive-learning methods for self-supervised feature repr…
Crop type maps are critical for tracking agricultural land use and estimating crop production. Remote sensing has proven an efficient and reliable tool for creating these maps in regions with abundant ground labels for model training, yet these labels remain difficult to obtain in many regions and years. NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, originally designed for forest monitoring, has shown promise for distinguishing tall and short crops. In the current study, we leverage GEDI to develop wall-to-wall maps of short vs tall crops on a global scale …
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）
While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose the first deep learning approach modeling simultaneously the inter- and intra-annual agricultural dynamics of parcel classification. Along with simple training adjustments, our model provides an improvement of over 6.3 mIoU points over the current state-of-the-art of crop classification. Furthermore, we release the first large-scale multi-year agricultural…
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）
GAF AG（GAF股份公司）；Technical University of Munich (TUM)（慕尼黑工业大学）
We present EuroCrops, a dataset based on self-declared field annotations for training and evaluating methods for crop type classification and mapping, together with its process of acquisition and harmonisation. By this, we aim to enrich the research efforts and discussion for data-driven land cover classification via Earth observation and remote sensing. Additionally, through inclusion of self-declarations gathered in the scope of subsidy control from all countries of the European Union (EU), this dataset highlights the difficulties and pitfalls one comes across when operating on a transnation…
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）
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）
Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. However, most crop mapping studies are limited to same-year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Classification accuracies of these methods degrade considerably in cross-year mapping. Cross-year crop mapping is more useful as it allows the prediction of the following years' crop maps using previously labeled data. We propose Vector Dynamic Time Warping (VDTW), a novel multi-year classification ap…
Mapping crops using remote sensing technology is important for food security and land management. Machine learning-based methods has become a popular approach for crop mapping in recent years. However, the key to machine learning, acquiring ample and accurate samples, is usually time-consuming and laborious. To solve this problem, a crop mapping method in the small sample/no sample case that integrating domain knowledge and using a cascaded classification framework that combine a weak classifier learned from samples with strong features and a strong classifier trained by samples with weak feat…
Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input from which to create crop type maps. Still, in many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models. When training data is not available in one region, classifiers trained in similar regions can be transferred, but shifts in the distribution of crop types as well as transformations of the features between r…
Politecnico di Torino（都灵理工大学）；Politecnico Interdepartmental Centre for Service Robotics（都灵理工大学跨部门服务机器人中心）；Università degli Studi di Torino（都灵大学）；Institute of Electronics, Computer and Telecommunication Engineering of the National Research Council of Italy（意大利国家研究委员会电子、计算机与电信工程研究所）
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are gr…
Pests and diseases are relevant factors for production losses in agriculture and, therefore, promote a huge investment in the prevention and detection of its causative agents. In many countries, Integrated Pest Management is the most widely used process to prevent and mitigate the damages caused by pests and diseases in citrus crops. However, its results are credited by humans who visually inspect the orchards in order to identify the disease symptoms, insects and mite pests. In this context, we design a weakly supervised learning process guided by saliency maps to automatically select regions…
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）
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）
Land cover classification in remote sensing is often faced with the challenge of limited ground truth. Incorporating historical information has the potential to significantly lower the expensive cost associated with collecting ground truth and, more importantly, enable early- and in-season mapping that is helpful to many pre-harvest decisions. In this study, we propose a new approach that can effectively transfer knowledge about the topology (i.e. relative position) of different crop types in the spectral feature space (e.g. the histogram of SWIR1 vs RDEG1 bands) to generate labels, thereby su…
Goldman School of Public Policy, University of California, Berkeley（加州大学伯克利分校高盛公共政策学院）
High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth labels for model training. However, these labels are absent in many regions, and models trained in other regions on typical satellite features, such as those from optical sensors, often exhibit low performance when transferred. Here we explore the use of NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, combined with Sentinel-2 optical data, for crop type mapping. Using data from three…
High efficiency in precision farming depends on accurate tools to perform weed detection and mapping of crops. This allows for precise removal of harmful weeds with a lower amount of pesticides, as well as increase of the harvest's yield by providing the farmer with valuable information. In this paper, we address the problem of fully automatic stem detection from image data for this purpose. Our approach runs on mobile agricultural robots taking RGB images. After processing the images to obtain a vegetation mask, our approach separates each plant into its individual leaves and later estimates …
Korea University（高丽大学）；National Observatory of Athens（雅典国家天文台）
The continuous increase in global population and the impact of climate change on crop production are expected to affect the food sector significantly. In this context, there is need for timely, large-scale and precise mapping of crops for evidence-based decision making. A key enabler towards this direction are new satellite missions that freely offer big remote sensing data of high spatio-temporal resolution and global coverage. During the previous decade and because of this surge of big Earth observations, deep learning methods have dominated the remote sensing and crop mapping literature. Ne…
Harvesting AlphaEarth: Benchmarking the Geospatial Foundation Model for Agricultural Downstream Tasks
Department of Earth System Science and Center on Food Security and the Environment, Stanford University, USA（地球系统科学系和食品安全与环境中心，斯坦福大学，美国）；Corteva Agriscience™, USA（科特维亚农业科学公司，美国）
AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Agriculture Mapping
Jockey Club STEM Lab of Quantitative Remote Sensing, Department of Geography, The University of Hong Kong, Hong Kong, China（香港大学地理系金钟STEM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学院）；School of Remote Sensing and Information Engineering, Wuhan University, China（武汉大学遥感与信息工程学院）
An Open Benchmark Dataset for GeoAI Foundation Models for Oil Palm Mapping in Indonesia
RECOFTC – The Center for People and Forests（RECOFTC——人民与森林研究中心）；Spatial Informatics Group, LLC（空间信息集团有限公司）；Faculty of Forestry, Hasanuddin University（哈桑丁大学林业学院）；Department of Forestry, Faculty of Agriculture, Universitas Riau（农业学院林业系，里亚大学）；University of San Francisco（圣弗朗西斯科大学）
European Commission, Joint Research Centre (JRC)（欧盟委员会联合研究中心）
Detailed parcel-level crop type mapping for the whole European Union (EU) is necessary for the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in particular, offers the opportunity to monitor agricultural land at a continental scale and in a timely manner. However, so far the potential of S1 has not been explored at such a scale. Capitalizing on the unique LUCAS 2018 Copernicus in-situ survey, we present the first continental crop type map at 10-m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018. Random …
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）
Spatial information on cropland distribution, often called cropland or crop maps, are critical inputs for a wide range of agriculture and food security analyses and decisions. However, high-resolution cropland maps are not readily available for most countries, especially in regions dominated by smallholder farming (e.g., sub-Saharan Africa). These maps are especially critical in times of crisis when decision makers need to rapidly design and enact agriculture-related policies and mitigation strategies, including providing humanitarian assistance, dispersing targeted aid, or boosting productivi…
BAWSeg: A UAV Multispectral Benchmark for Barley Weed Segmentation
Department of Computer Science and Software Engineering, The University of Western Australia（计算机科学与软件工程系，西澳大学）；Department of Primary Industries and Regional Development (DPIRD), Government of Western Australia（初级产业与地区发展部（DPIRD），西澳政府）
Satellite monitoring uncovers progress but large disparities in doubling crop yields
Mortenson Center in Global Engineering & Resilience, University of Colorado Boulder（科罗拉多大学博尔德分校莫滕森全球工程与韧性中心）；Better Planet Lab, University of Colorado Boulder（科罗拉多大学博尔德分校更美好星球实验室）；University of Colorado Boulder（科罗拉多大学博尔德分校）
The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultu…
Lincoln Centre for Autonomous Systems (LCAS)（林肯自主系统中心（LCAS））；University of Lincoln（林肯大学）
Long-term autonomy is one of the most demanded capabilities looked into a robot. The possibility to perform the same task over and over on a long temporal horizon, offering a high standard of reproducibility and robustness, is appealing. Long-term autonomy can play a crucial role in the adoption of robotics systems for precision agriculture, for example in assisting humans in monitoring and harvesting crops in a large orchard. With this scope in mind, we report an ongoing effort in the long-term deployment of an autonomous mobile robot in a vineyard for data collection across multiple months. …
Technical University of Munich（慕尼黑工业大学）；Toyota Research Institute（丰田研究院）；Siemens Corporate Technology（西门子公司技术部）
We present a novel approach to tackle domain adaptation between synthetic and real data. Instead, of employing "blind" domain randomization, i.e., augmenting synthetic renderings with random backgrounds or changing illumination and colorization, we leverage the task network as its own adversarial guide toward useful augmentations that maximize the uncertainty of the output. To this end, we design a min-max optimization scheme where a given task competes against a special deception network to minimize the task error subject to the specific constraints enforced by the deceiver. The deception net…
SpectralEarth: Training Hyperspectral Foundation Models at Scale
Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM)（地球观测数据科学教授职位（SiPEO），慕尼黑技术大学）；Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)（遥感技术研究所（IMF），德国航空航天中心（DLR））；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学，法国国家科学研究中心（CNRS），格勒诺布尔高等理工学院（Grenoble INP），LJK）
Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region
Empa, Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术实验室）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Swiss Data Science Center, ETH Zurich and EPFL（瑞士数据科学中心（苏黎世联邦理工学院和洛桑联邦理工学院））
Predictive maintenance, i.e. predicting failure to be few steps ahead of the fault, is one of the pillars of Industry 4.0. An effective method for that is to track early signs of degradation before a failure happens. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of full spectrum of the vibration data caused by the machines and data visualization technologies. This scheme is featured by no training data required and by quick start after installation. First, we propose to use full spectrum (as high-dimensional data vector) with no …
University of Minnesota（明尼苏达大学）；University of Pittsburgh（匹兹堡大学）
The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping. However, such mapping effort is challenging due to the existence of various land cover classes, noisy data, and the lack of proper labels. Also, each land cover class typically has its own unique temporal pattern and can be identified only during certain periods. In this article, we introduce a novel architecture that incorporates the UNet structure with Bidirectional LSTM and Attention mechanism to jointly exploit the spatial and temporal nature of satellite data and to b…
The unprecedented availability of spatial and temporal high-resolution satellite image time series (SITS) for crop type mapping is believed to necessitate deep learning architectures to accommodate challenges arising from both dimensions. Recent state-of-the-art deep learning models have shown promising results by stacking spatial and temporal encoders. However, we present a Pyramid Time-Series Transformer (PTST) that operates solely on the temporal dimension, i.e., neglecting the spatial dimension, can produce superior results with a drastic reduction in GPU memory consumption and easy extens…
