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 limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.
Vehicle speed dataset for the major European road network derived from Sentinel-2 imagery, 2022-2026
Heidelberg Institute for Geoinformation Technology (HeiGIT)（海德堡地理信息技术研究所）；Faculty of Geographical Sciences, University of Lodz（罗兹大学地理科学学院）；GIScience Research Group, Institute of Geography, University of Heidelberg（海德堡大学地理研究所地理信息科学研究组）
Evaluating the Efficacy of Sentinel-2 versus Aerial Imagery in Serrated Tussock Classification
School of Information Technology, Deakin University, Australia（信息技术学院，德堪大学，澳大利亚）；Agriculture Victoria Research, Department of Energy, Environment and Climate Action, Australia（维多利亚农业研究，能源、环境与气候变化行动部，澳大利亚）；Institute of Innovation, Science and Sustainability, Federation University, Australia（创新、科学与可持续性研究所，联邦大学，澳大利亚）
University of Winnipeg（温尼伯大学）；Norwegian University of Science & Technology (NTNU) Gjøvik（挪威科技大学（NTNU）约维克校区）
The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I …
TU Kaiserslautern（凯撒斯劳滕工业大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an ov…
Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and valuable information at global scale that can be used to develop land cover classification models. However, such a global application requires a geographically diverse training dataset. Here, we present LandCoverNet, a global training dataset for land cover classification based on Sentinel-2 observations at 10m spatial resolution. Land cover class labels are defined based on annual time-series of Sentinel-2, and verifie…
Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息学研究所）；Space Research Centre, Polish Academy of Sciences（波兰科学院空间研究中心）
Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods
Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学（LUMS））
SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining
Chair of Data Science in Earth Observation（地球观测数据科学主任）；Technical University of Munich（慕尼黑技术大学）；Remote Sensing Technology Institute（遥感技术研究所）；German Aerospace Center (DLR)（德国航空航天中心）；Department of Aerospace Engineering（航空航天工程系）；University of the Bundeswehr Munich（联邦国防军慕尼黑大学）；LEAP；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inria（法国国家信息与自动化技术研究院）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔INP）；LJK
Supervised and self-supervised land-cover segmentation & classification of the Biesbosch wetlands
ESRIN, European Space Agency（欧洲空间局ESRIN）；Dept. of Aerospace Engineering, University of Naples Federico II（那不勒斯费德里科二世大学航空航天工程系）；University of Amsterdam（阿姆斯特丹大学）
DengueNet: Dengue Prediction using Spatiotemporal Satellite Imagery for Resource-Limited Countries
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）；University College Dublin（都柏林大学学院）；University of Cauca（考卡大学）；Indiana University – Purdue University Indianapolis（印第安纳大学与普渡大学印第安纳波利斯联合分校）；Worcester Polytechnic Institute（伍斯特理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Assessing the value of Geo-Foundational Models for Flood Inundation Mapping: Benchmarking models for Sentinel-1, Sentinel-2, and Planetscope for end-users
School of Computing, University of Portsmouth（朴茨茅斯大学计算学院）
Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully connected neural networks for semantic segmentation. The benchmark datasets available for training deep segmentation models in remote sensing imagery tend to be small, however, often consisting of only a handful of images from a single location with a single scale. This limits the models' ability to generalize to other datasets. Domain adaptation has been proposed to improve the models' generalization but we find these ap…
This work presents SeasoNet, a new large-scale multi-label land cover and land use scene understanding dataset. It includes $1\,759\,830$ images from Sentinel-2 tiles, with 12 spectral bands and patch sizes of up to $ 120 \ \mathrm{px} \times 120 \ \mathrm{px}$. Each image is annotated with large scale pixel level labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018 and a five times smaller minimum mapping unit (MMU) than the original CLC maps. We provide pixel synchronous examples from all four seasons, plus an additiona…
Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications. Building supervised machine learning models based on these imagery, however, may require ground reference labels which are not available at global scale. Here, we propose a generative model to produce multi-resolution multi-spectral imagery based on Sentinel-2 data. The resulting synthetic images are indistinguishable from real ones by humans. This technique paves the road for future work to generate labeled synthetic imagery that can be used for data augmentation in …
The University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；Applied Research Associates（应用研究联合公司）
Land cover classification of satellite imagery is an important step toward analyzing the Earth's surface. Existing models assume a closed-set setting where both the training and testing classes belong to the same label set. However, due to the unique characteristics of satellite imagery with an extremely vast area of versatile cover materials, the training data are bound to be non-representative. In this paper, we study the problem of open-set land cover classification that identifies the samples belonging to unknown classes during testing, while maintaining performance on known classes. Altho…
Wageningen University（瓦赫宁根大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change detection, we explore methods that exploit the temporal consistency of Sentinel-2 times series to obtain a usable self-supervised learning signal. For this, we build and make publicly available (https://zenodo.org/record/4280482) the Sentinel-2 Multitemporal Cities Pairs (S2MTCP) dataset, containing multitemporal image pairs from 1520 urban a…
Smith School of Enterprise and the Environment（史密斯企业与环境学院）；School of Geography and the Environment（地理与环境学院）；University of Oxford（牛津大学）
Earth observation offers new insight into anthropogenic changes to nature, and how these changes are effecting (and are effected by) the built environment and the real economy. With the global availability of medium-resolution (10-30m) synthetic aperture radar (SAR) Sentinel-1 and multispectral Sentinel-2 imagery, machine learning can be employed to offer these insights at scale, unbiased to the reporting of companies and countries. In this paper, I introduce DeepSentinel, a data pipeline and experimentation framework for producing general-purpose semantic embeddings of paired Sentinel-1 and S…
Automated Linear Disturbance Mapping via Semantic Segmentation of Sentinel-2 Imagery
University of Winnipeg（温尼伯大学）；Hatfield Consultants Group（哈特菲尔德咨询集团）；University of Manitoba（曼尼托巴大学）；Pachama（帕查玛公司）；Environment and Climate Change Canada（加拿大环境与气候变化部）
Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network's feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly …
University of Oxford（牛津大学）；University of Valencia（瓦伦西亚大学）
High resolution remote sensing imagery is used in broad range of tasks, including detection and classification of objects. High-resolution imagery is however expensive, while lower resolution imagery is often freely available and can be used by the public for range of social good applications. To that end, we curate a multi-spectral multi-image super-resolution dataset, using PlanetScope imagery from the SpaceNet 7 challenge as the high resolution reference and multiple Sentinel-2 revisits of the same imagery as the low-resolution imagery. We present the first results of applying multi-image s…
Technical University of Munich（慕尼黑工业大学）；German Aerospace Center (DLR)（德国航空航天中心（DLR））
The availability of curated large-scale training data is a crucial factor for the development of well-generalizing deep learning methods for the extraction of geoinformation from multi-sensor remote sensing imagery. While quite some datasets have already been published by the community, most of them suffer from rather strong limitations, e.g. regarding spatial coverage, diversity or simply number of available samples. Exploiting the freely available data acquired by the Sentinel satellites of the Copernicus program implemented by the European Space Agency, as well as the cloud computing facili…
ForCM: Forest Cover Mapping from Multispectral Sentinel-2 Image by Integrating Deep Learning with Object-Based Image Analysis
BRAC University（布拉克大学）；Charles Sturt University（查尔斯·斯特劳特大学）；University of New England（新英格兰大学）；University of Technology Sydney（技术悉尼大学）；Griffith University（格里菲斯大学）；Universiti Kebangsaan Malaysia (UKM)（马来亚大学）；Federation University Australia（联邦大学澳大利亚）；RMIT University（皇家墨尔本理工大学）
Carted；NVIDIA（英伟达）；Frontier Development Lab（前沿发展实验室）；SpaceML
Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies. The NASA Impact Flood Detection competition tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. We propose a semi-supervised learning pseudo-labeling scheme that derives confidence estimates from U-Net ensembles, progressively improving accuracy. Concretely, we use a cyclical approach involving multiple stages (1) training an ensemble model of multiple U-Net architectures with the …
Flow matching for Sentinel-2 super-resolution: implementation, application, and implications
Department of Agricultural and Biological Engineering, Mississippi State University（农业与生物工程系，密苏里州立大学）；North Mississippi Research and Extension Center, Mississippi State University（北密苏里研究与推广中心，密苏里州立大学）
European Commission, Joint Research Centre (JRC)（欧洲委员会联合研究中心）；Arhs Developments S.A（Arhs开发公司）；Engineering S.p.a（工程股份公司）
Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world.The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale.This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery.A multi-neuro modeling methodology bu…
Physics informed Transformer-VAE for biophysical parameter estimation: PROSAIL model inversion in Sentinel-2 imagery
African Institute for Mathematical Sciences (AIMS), Accra, Ghana（非洲数学科学研究所（AIMS））；African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa（非洲数学科学研究所（AIMS））
IRSTEA（法国国家农业、食品与环境研究院）；University of Montpellier（蒙彼利埃大学）
Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring. Many studies have been conducted using one of the two sources, but how to smartly combine the complementary information provided by radar and optical SITS is still an open challenge. In this context, we propose a new neural architecture for the combination of Sentinel-1 (S1) and Sentinel-2 (S2) imagery at object level, applied to a real-world land cover classi…
CloudBreaker: Breaking the Cloud Covers of Sentinel-2 Images using Multi-Stage Trained Conditional Flow Matching on Sentinel-1
Computer Science and Engineering（计算机科学与工程）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Institute of Water and Flood Management（水与洪水管理研究所）
Otto-von-Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；BASF Digital Farming GmbH（巴斯夫数字农业有限公司）
Landsat-8 (NASA) and Sentinel-2 (ESA) are two prominent multi-spectral imaging satellite projects that provide publicly available data. The multi-spectral imaging sensors of the satellites capture images of the earth's surface in the visible and infrared region of the electromagnetic spectrum. Since the majority of the earth's surface is constantly covered with clouds, which are not transparent at these wavelengths, many images do not provide much information. To increase the temporal availability of cloud-free images of a certain area, one can combine the observations from multiple sources. H…
SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting
Mediterranean Agronomic Institute of Montpellier - CIHEAM-IAMM（地中海农业研究院-CIHEAM-IAMM）；Inria（法国国家信息与自动化技术研究院）；INRAE（法国国家农业研究咨询中心）；Cirad（国际热带农业研究中心）；UMR TETIS（TETIS联合研究单位）；Univ. of Montpellier（蒙彼利埃大学）
