在本工作中，我们介绍了Sen4AgriNet，一个基于Sentinel-2的时间序列、多国基准数据集，专门用于农业监测应用，结合机器学习和深度学习。Sen4AgriNet数据集从通过土地 Parcel Identification System (LPIS)收集的农民声明中进行标注，以协调全国范围内的标签。这些声明只有最近才作为开放数据发布，从而使卫星图像的标注首次能够基于真实数据进行。我们随后提出并标准化了一个新的作物类型分类法，覆盖欧洲，以满足Common Agriculture Policy (CAP)的需求，基于Food and Agriculture Organization (FAO)的指示性作物分类方案。Sen4AgriNet是唯一一个包含所有光谱信息的多国、多年度数据集。它被构建以覆盖2016-2020年期间的加泰罗尼亚和法国，同时可以扩展以包括更多国家。目前，它包含4250万个地块，使其显著大于其他可用档案。我们提取了两个子数据集以突出其在各种深度学习应用中的价值；对象聚合数据集（OAD）和碎片组装数据集（PAD）。OAD利用每个地块的区域统计信息，从而创建一个强大的标签到特征实例用于分类算法。另一方面，PAD将分类问题推广到地块提取和语义分割和标注。PAD和OAD在三种不同的场景下进行测试，以展示和建模不同年份和不同国家之间的空间和时间变化的影响。
英文摘要：
In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing country wide labels. These declarations have only recently been made available as open data, allowing for the first time the labeling of satellite imagery from ground truth data. We proceed to propose and standardise a new crop type taxonomy across Europe that address Common Agriculture Policy (CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year dataset that includes all spectral information. It is constructed to cover the period 2016-2020 for Catalonia and France, while it can be extended to include additional countries. Currently, it contains 42.5 million parcels, which makes it significantly larger than other available archives. We extract two sub-datasets to highlight its value for diverse Deep Learning applications; the Object Aggregated Dataset (OAD) and the Patches Assembled Dataset (PAD). OAD capitalizes zonal statistics of each parcel, thus creating a powerful label-to-features instance for classification algorithms. On the other hand, PAD structure generalizes the classification problem to parcel extraction and semantic segmentation and labeling. The PAD and OAD are examined under three different scenarios to showcase and model the effects of spatial and temporal variability across different years and different countries.
Technical University of Munich（慕尼黑工业大学）；German Aerospace Center（德国航空航天中心）；Aarhus University（奥胡斯大学）
Human settlement extent (HSE) information is a valuable indicator of world-wide urbanization as well as the resulting human pressure on the natural environment. Therefore, mapping HSE is critical for various environmental issues at local, regional, and even global scales. This paper presents a deep-learning-based framework to automatically map HSE from multi-spectral Sentinel-2 data using regionally available geo-products as training labels. A straightforward, simple, yet effective fully convolutional network-based architecture, Sen2HSE, is implemented as an example for semantic segmentation w…
University of Minnesota（明尼苏达大学）；University of Pittsburgh（匹兹堡大学）
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized computer vision applications can accelerate development of novel crop mapping techniques. Currently, the United States Department of Agriculture (USDA) annually releases the Cropland Data Layer (CDL) which contains crop labels at 30m resolution for the entire United States of America. While CDL is state of the art and is widely used for a number of agricultural applications, it has a number of limitations (e.g., pixelated…
GFZ German Research Centre for Geosciences（德国地学研究中心）；Universitat de València（瓦伦西亚大学）；University of Twente（特文特大学）；Nanjing University（南京大学）
Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for advance…
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan
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）
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 …
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…
Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon and Switzerland. A deep convolutional neural network (CNN) was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height. In Gabon, reference heights for training and validation were derived from airborne LiDAR measurements. In Switzerland, reference heights were taken from an existing canopy height model derived via photogrammetric surface reconstruction. The resulting maps have a mean absolute error (MA…
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…
Country-wide, high-resolution monitoring of forest browning with Sentinel-2
ETH Zurich（苏黎世联邦理工学院）；ETH AI Center（ETH人工智能中心）；Swiss Data Science Center（瑞士数据科学中心）；EPFL（洛桑联邦理工学院）；Swiss Federal Institute for Forest, Snow and Landscape Research WSL（瑞士联邦森林、雪与景观研究所）；University of Bern（伯尔尼大学）；Oeschger Centre for Climate Change Research（厄施格气候变化研究中心）
Indian Licence Plate Detection is a problem that has not been explored much at an open-source level.There are proprietary solutions available for it, but there is no big open-source dataset that can be used to perform experiments and test different approaches.Most of the large datasets available are for countries like China, Brazil, but the model trained on these datasets does not perform well on Indian plates because the font styles and plate designs used vary significantly from country to country.This paper introduces an Indian license plate dataset with 16192 images and 21683 plate plates a…
Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datasets have significant limitations. To generate an open and comprehensive training library of high resolution Earth imagery and high quality land cover classifications, public Sentinel-2 data at 10 m spatial resolution was matched with accurate GlobeLand30 labels from 2010, which were filtered by agreement with an intermediary Sentinel-2 classification at 20 m produced during atmospheric correction. Scene-level classifi…
Makerere University（麦克雷雷大学）；National Crops Resources Research Institute（国家作物资源研究所）
Pests and diseases pose a key challenge to passion fruit farmers across Uganda and East Africa in general. They lead to loss of investment as yields reduce and losses increases. As the majority of the farmers, including passion fruit farmers, in the country are smallholder farmers from low-income households, they do not have the sufficient information and means to combat these challenges. While, passion fruits have the potential to improve the well-being of these farmers as they have a short maturity period and high market value , without the required knowledge about the health of their crops,…
Evaluating the Role of Training Data Origin for Country-Scale Cropland Mapping in Data-Scarce Regions: A Case Study of Nigeria
ETH Zurich（苏黎世联邦理工学院）；Swiss Federal Laboratories for Material Science and Technology（瑞士材料科学与技术联邦实验室）；Swiss Data Science Center（瑞士数据科学中心）；EPFL（洛桑联邦理工学院）；Wageningen University & Research（瓦赫宁根大学与研究中心）
A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction
Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Indore, Indore 453552, India（计算机科学与工程系，印度理工学院（IIT）印多尔，印多尔453552，印度）
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…
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation
Arizona State University（亚利桑那州立大学）；Microsoft AI for Good Research Lab（微软人工智能造福社会研究实验室）；Taylor Geospatial Institute（泰勒地理空间研究所）；Washington University in St Louis（圣路易斯华盛顿大学）；Taylor Geospatial Engine（泰勒地理空间引擎）
Applications of digital agricultural services often require either farmers or their advisers to provide digital records of their field boundaries. Automatic extraction of field boundaries from satellite imagery would reduce the reliance on manual input of these records which is time consuming and error-prone, and would underpin the provision of remote products and services. The lack of current field boundary data sets seems to indicate low uptake of existing methods,presumably because of expensive image preprocessing requirements and local, often arbitrary, tuning. In this paper, we address th…
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classifying land use change without high resolution imagery offers the potential to monitor vital aspects of global development agenda including climate smart agriculture, drought resistant crops, and sustainable land management. Utilizing a combination of capsule layers and long-short…
NORCE, The Norwegian Research Centre AS（挪威研究中心NORCE）；UiT the Arctic University of Norway（挪威北极大学UiT）
In this paper, we explore the possibility of detecting polar lows in C-band SAR images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absence of a maritime mesocyclone, respectively. The dataset is constructed using the ERA5 dataset as baseline and it consists of 2004 annotated images. To our knowledge, this is the first dataset of its kind to be publicly released. The dataset is used to train a deep learning model to classify the labeled images. Evaluated on an independent test set, …
Towards more efficient agricultural practices via transformer-based crop type classification
Gobierno del estado de Jalisco（哈利斯科州政府）；TecMM Unidad Zapopan（TecMM萨波潘分部）；Climate Change AI（气候变化人工智能）；Kabarak University（卡巴拉大学）；Aspia Space（阿斯皮亚航天）；Columbia University（哥伦比亚大学）
ETH Zurich（苏黎世联邦理工学院）；Agroscope（瑞士农业研究中心）；Federal Office for Agriculture, Switzerland（瑞士联邦农业办公室）；University of Zurich（苏黎世大学）
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 a…
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））
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 fo…
European Commission's Joint Research Center（欧盟委员会联合研究中心）
Accurate, detailed, and timely crop type mapping is a very valuable information for the institutions in order to create more accurate policies according to the needs of the citizens. In the last decade, the amount of available data dramatically increased, whether it can come from Remote Sensing (using Copernicus Sentinel-2 data) or directly from the farmers (providing in-situ crop information throughout the years and information on crop rotation). Nevertheless, the majority of the studies are restricted to the use of one modality (Remote Sensing data or crop rotation) and never fuse the Earth …
In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, and assess their performance on a large dataset of freely available Sentinel-2 imagery. We find that the best-performing approaches are hybrid configurations for which most of the parameters (up to 90%) are allocated to modeling the temporal structure…
Charles Sturt University（查尔斯特大学）；Feni University（费尼大学）
A staple food in more than a hundred nations worldwide is rice (Oryza sativa). The cultivation of rice is vital to global economic growth. However, the main issue facing the agricultural industry is rice leaf disease. The quality and quantity of the crops have declined, and this is the main cause. As farmers in any country do not have much knowledge about rice leaf disease, they cannot diagnose rice leaf disease properly. That's why they cannot take proper care of rice leaves. As a result, the production is decreasing. From literature survey, it has seen that YOLOv5 exhibit the better result c…
ETH Zurich（苏黎世联邦理工学院）；Norwegian Institute of Bioeconomy Research（挪威生物经济研究所）；University of Zurich（苏黎世大学）
Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-meter resolution, using freely available satellite imagery as input. Our method jointly tra…
Federal University of São Carlos（圣卡洛斯联邦大学）；John Deere（约翰迪尔）
In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed varieties, pesticides, and fertilizers. However, manually identifying productive fields is often a time-consuming and error-prone task. Previous studies explore different methods to detect crop fields using advanced machine learning algorithms, but they often lack good quality labeled data. In this context, we propose a high-quality dataset generated by machine operation combined with Sentinel-2 images tracked over ti…
Politecnico di Torino（都灵理工大学）；International Islamic University（国际伊斯兰大学）
The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time consuming solution that poses strict limita…
India is an agriculture-dependent country. As we all know that farming is the backbone of our country it is our responsibility to preserve the crops. However, we cannot stop the destruction of crops by natural calamities at least we have to try to protect our crops from diseases. To, detect a plant disease we need a fast automatic way. So, this paper presents a model to identify the particular disease of plant leaves at early stages so that we can prevent or take a remedy to stop spreading of the disease. This proposed model is made into five sessions. Image preprocessing includes the enhancem…
Cropland PAtteRNS: Parallel Dimensional Attention Networks and Attention to Dataset Disparity for Crop Segmentation in Satellite Imagery Time Series Data
One of the critical biotic stress factors paddy farmers face is diseases caused by bacteria, fungi, and other organisms. These diseases affect plants' health severely and lead to significant crop loss. Most of these diseases can be identified by regularly observing the leaves and stems under expert supervision. In a country with vast agricultural regions and limited crop protection experts, manual identification of paddy diseases is challenging. Thus, to add a solution to this problem, it is necessary to automate the disease identification process and provide easily accessible decision support…
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…
Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data
Centro Nacional de Inteligencia Artificial (CENIA)（国家人工智能中心）；European Commission, Joint Research Centre (JRC)（欧盟委员会联合研究中心）；Technical University of Munich（慕尼黑工业大学）
