在深度学习时代,注释的数据集已成为遥感社区的关键资产。在过去的十年中,发表了许多不同的数据集,每个数据集都为特定的数据类型以及特定的任务或应用程序设计。在遥感数据集的丛林中,很难跟踪已经可用的内容。在本文中,我们介绍了EOD -IEEE GRSS地球观察数据库(EOD) - 一个交互式在线平台,用于分类不同类型的数据集利用遥感图像。
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通过卫星图像和机器学习对行星进行大规模分析是一个梦想,这一梦想不断受到难以获取高度代表性的高分辨率图像的成本的阻碍。为了纠正此问题,我们在这里介绍WorldStrat数据集。 The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's Phi-Lab as part of the ESA-funded QueryPlanet project, we curate nearly 10,000独特位置的SQKM,以确保全世界所有类型的土地用途分层:从农业到冰盖,从森林到多种城市化密度。我们还丰富了通常在ML数据集中代表不足的地点的人:人道主义兴趣的地点,非法采矿地点以及有风险的人的定居点。我们以10 m/pixel的可自由访问的下分辨率Sentinel-2卫星的多个低分辨率图像为暂时匹配每个高分辨率图像。我们伴随着该数据集的开源Python软件包,以:重建或扩展WorldStrat数据集,训练和推断基线算法,并使用丰富的教程学习,所有这些都与流行的EO-Learn Toolbox兼容。我们特此希望能够促进ML在卫星图像中的广泛应用,并可能从免费的公共低分辨率Sentinel2图像中发展出昂贵的私人高分辨率图像所允许的相同的分析能力。我们通过训练并发布了有关多帧超分辨率任务的几个高度计算效率的基线来说明这一特定点。高分辨率空中图像是CC BY-NC,而标签和Sentinel2图像为CC,而BSD下的源代码和预训练模型。该数据集可从https://zenodo.org/record/6810792获得,并在https://github.com/worldstrat/worldstrat上获得。
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Sky-image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty in solar power generation. However, one of the biggest challenges is the lack of massive and diversified sky image samples. In this study, we present a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting (i.e., forecasting horizon less than 30 minutes), as well as related research areas which can potentially help improve solar forecasting methods, including cloud segmentation, cloud classification and cloud motion prediction. We first identify 72 open-source sky image datasets that satisfy the needs of machine/deep learning. Then a database of information about various aspects of the identified datasets is constructed. To evaluate each surveyed datasets, we further develop a multi-criteria ranking system based on 8 dimensions of the datasets which could have important impacts on usage of the data. Finally, we provide insights on the usage of these datasets for different applications. We hope this paper can provide an overview for researchers who are looking for datasets for very short-term solar forecasting and related areas.
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DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-and machine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.
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近年来,地理空间行业一直在稳定发展。这种增长意味着增加卫星星座,每天都会产生大量的卫星图像和其他遥感数据。有时,这些信息,即使在某些情况下我们指的是公开可用的数据,由于它的大小,它也无法占据。从时间和其他资源的角度来看,借助人工或使用传统的自动化方法来处理如此大量的数据并不总是可行的解决方案。在目前的工作中,我们提出了一种方法,用于创建一个由公开可用的遥感数据组成的多模式和时空数据集,并使用ART机器学习(ML)技术进行可行性进行测试。确切地说,卷积神经网络(CNN)模型的用法能够分离拟议数据集中存在的不同类别的植被。在地理信息系统(GIS)和计算机视觉(CV)的背景下,类似方法的受欢迎程度和成功更普遍地表明,应考虑并进一步分析和开发方法。
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深度学习模式和地球观察的协同组合承诺支持可持续发展目标(SDGS)。新的发展和夸张的申请已经在改变人类将面临生活星球挑战的方式。本文审查了当前对地球观测数据的最深入学习方法,以及其在地球观测中深度学习的快速发展受到影响和实现最严重的SDG的应用。我们系统地审查案例研究至1)实现零饥饿,2)可持续城市,3)提供保管安全,4)减轻和适应气候变化,5)保留生物多样性。关注重要的社会,经济和环境影响。提前令人兴奋的时期即将到来,算法和地球数据可以帮助我们努力解决气候危机并支持更可持续发展的地方。
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The number of scientific publications continues to rise exponentially, especially in Computer Science (CS). However, current solutions to analyze those publications restrict access behind a paywall, offer no features for visual analysis, limit access to their data, only focus on niches or sub-fields, and/or are not flexible and modular enough to be transferred to other datasets. In this thesis, we conduct a scientometric analysis to uncover the implicit patterns hidden in CS metadata and to determine the state of CS research. Specifically, we investigate trends of the quantity, impact, and topics for authors, venues, document types (conferences vs. journals), and fields of study (compared to, e.g., medicine). To achieve this we introduce the CS-Insights system, an interactive web application to analyze CS publications with various dashboards, filters, and visualizations. The data underlying this system is the DBLP Discovery Dataset (D3), which contains metadata from 5 million CS publications. Both D3 and CS-Insights are open-access, and CS-Insights can be easily adapted to other datasets in the future. The most interesting findings of our scientometric analysis include that i) there has been a stark increase in publications, authors, and venues in the last two decades, ii) many authors only recently joined the field, iii) the most cited authors and venues focus on computer vision and pattern recognition, while the most productive prefer engineering-related topics, iv) the preference of researchers to publish in conferences over journals dwindles, v) on average, journal articles receive twice as many citations compared to conference papers, but the contrast is much smaller for the most cited conferences and journals, and vi) journals also get more citations in all other investigated fields of study, while only CS and engineering publish more in conferences than journals.
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这项研究介绍了\ textit {landslide4sense},这是一种从遥感中检测到滑坡检测的参考基准。该存储库具有3,799个图像贴片,可从Sentinel-2传感器中融合光学层,并带有数字高程模型和来自ALOS Palsar的斜率层。附加的地形信息促进了对滑坡边界的准确检测,而最近的研究表明,仅使用光学数据,这是具有挑战性的。广泛的数据集支持在滑坡检测中进行深度学习(DL)研究,以及用于系统更新滑坡库存的方法的开发和验证。基准数据集已在四个不同的时间和地理位置收集:伊伯里(2018年9月),科达古(2018年8月),戈尔卡(2015年4月)和台湾(2009年8月)。每个图像像素均标记为属于滑坡,包括各种来源和彻底的手动注释。然后,我们评估11个最先进的DL分割模型的滑坡检测性能:U-NET,RESU-NET,PSPNET,CONTECTNET,DEEPLAB-V2,DEEPLAB-V3+,FCN-8,LINKNET,FRRRN-A,FRRN-A,, FRRN-B和SQNET。所有型号均已从划痕上对每个研究区域的四分之一的补丁进行培训,并在其他三个季度的独立贴片上进行了测试。我们的实验表明,Resu-NET的表现优于其他模型,用于滑坡检测任务。我们在\ url {www.landslide4sense.org}公开获得多种源滑坡基准数据(Landslide4sense)和经过测试的DL模型,为遥感,计算机视觉和机器学习社区建立了重要的资源通常,尤其是对滑坡检测的应用。
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海洋正在经历前所未有的快速变化,在负责任管理所需的时空尺度上,视觉监测海洋生物群是一项艰巨的任务。由于研究界寻求基准,因此所需的数据收集的数量和速率迅速超过了我们处理和分析它们的能力。机器学习的最新进展可以对视觉数据进行快速,复杂的分析,但由于缺乏数据标准化,格式不足以及对大型标签数据集的需求,在海洋中取得了有限的成功。为了满足这一需求,我们构建了Fathomnet,这是一个开源图像数据库,该数据库标准化和汇总了经过精心策划的标记数据。 Fathomnet已被海洋动物,水下设备,碎片和其他概念的现有标志性和非偶像图像所播种,并允许分布式数据源的未来贡献。我们展示了如何使用Fathomnet数据在其他机构视频上训练和部署模型,以减少注释工作,并在与机器人车辆集成时启用自动跟踪水下概念。随着Fathomnet继续增长并结合了社区的更多标记数据,我们可以加速视觉数据以实现健康且可持续的全球海洋。
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Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.
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With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets. Many studies use their own data at small spatio-temporal scales, and demonstrate an application of an existing or adapted data science method for a particular task. This approach often involves intensive and time-consuming data collection and processing, but generates results restricted to specific ecosystems and sensor types. There is a lack of widespread acknowledgement of how the types and structures of data used affects performance and accuracy of analysis algorithms. To accelerate progress in the field more efficiently, benchmarking datasets upon which methods can be tested and compared are sorely needed. Here, we discuss how lack of standardisation impacts confidence in estimation of key forest properties, and how considerations of data collection need to be accounted for in assessing method performance. We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data. We list a set of example large-scale datasets that could contribute to benchmarking, and present a vision for how community-driven, representative benchmarking initiatives could benefit the field.
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The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 2nd International Workshop on Reading Music Systems, held in Delft on the 2nd of November 2019.
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Remote sensing images are useful for a wide variety of environmental and earth monitoring tasks, including tracking deforestation, illegal fishing, urban expansion, and natural disasters. The earth is extremely diverse -- the amount of potential tasks in remote sensing images is massive, and the sizes of features range from several kilometers to just tens of centimeters. However, creating generalizable computer vision methods is a challenge in part due to the lack of a large-scale dataset that captures these diverse features for many tasks. In this paper, we present Satlas, a remote sensing dataset and benchmark that is large in both breadth, featuring all of the aforementioned applications and more, as well as scale, comprising 290M labels under 137 categories and seven label modalities. We evaluate eight baselines and a proposed method on Satlas, and find that there is substantial room for improvement in addressing research challenges specific to remote sensing, including processing image time series that consist of images from very different types of sensors, and taking advantage of long-range spatial context. We also find that pre-training on Satlas substantially improves performance on downstream tasks with few labeled examples, increasing average accuracy by 16% over ImageNet and 5% over the next best baseline.
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社交媒体有可能提供有关紧急情况和突然事件的及时信息。但是,在每天发布的数百万帖子中找到相关信息可能很困难,并且开发数据分析项目通常需要时间和技术技能。这项研究提出了一种为分析社交媒体的灵活支持的方法,尤其是在紧急情况下。引入了可以采用社交媒体分析的不同用例,并讨论了从大量帖子中检索信息的挑战。重点是分析社交媒体帖子中包含的图像和文本,以及一组自动数据处理工具,用于过滤,分类和使用人类的方法来支持数据分析师的内容。这种支持包括配置自动化工具的反馈和建议,以及众包收集公民的投入。通过讨论Crowd4SDG H2020欧洲项目中开发的三个案例研究来验证结果。
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小型太阳能光伏(PV)阵列中电网的有效集成计划需要访问高质量的数据:单个太阳能PV阵列的位置和功率容量。不幸的是,不存在小型太阳能光伏的国家数据库。那些确实有限的空间分辨率,通常汇总到州或国家一级。尽管已经发布了几种有希望的太阳能光伏检测方法,但根据研究,研究这些模型的性能通常是高度异质的。这些方法对能源评估的实际应用的比较变得具有挑战性,可能意味着报告的绩效评估过于乐观。异质性有多种形式,我们在这项工作中探讨了每种形式:空间聚集的水平,地面真理的验证,培训和验证数据集的不一致以及培训的位置和传感器的多样性程度和验证数据始发。对于每个人,我们都会讨论文献中的新兴实践,以解决它们或暗示未来研究的方向。作为调查的一部分,我们评估了两个大区域的太阳PV识别性能。我们的发现表明,由于验证过程中的共同局限性,从卫星图像对太阳PV自动识别的传统绩效评估可能是乐观的。这项工作的收获旨在为能源研究人员和专业人员提供自动太阳能光伏评估技术的大规模实用应用。
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Masader(Alyafeai等,2021)创建了一种元数据结构,用于分类阿拉伯NLP数据集。但是,开发一种简单的方法来探索这种目录是一项艰巨的任务。为了为探索目录的用户和研究人员提供最佳体验,必须解决一些设计和用户体验的挑战。此外,用户与网站的交互可能提供了一种简单的方法来改善目录。在本文中,我们介绍了Masader Plus,该网络接口供用户浏览masader。我们演示了数据探索,过滤和简单的API,该API允许用户从后端检查数据集。可以使用此链接https://arbml.github.io/masader探索masader plus。可以在此处找到的视频录制说明界面的录制https://www.youtube.com/watch?v=setDlseqchk。
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问答系统被认为是流行且经常有效的信息在网络上寻求信息的手段。在这样的系统中,寻求信息者可以通过自然语言提出问题来获得对他们的查询的简短回应。交互式问题回答是一种最近提出且日益流行的解决方案,它位于问答和对话系统的交集。一方面,用户可以以普通语言提出问题,并找到对她的询问的实际回答;另一方面,如果在初始请求中有多个可能的答复,很少或歧义,则系统可以将问题交通会话延长到对话中。通过允许用户提出更多问题,交互式问题回答使用户能够与系统动态互动并获得更精确的结果。这项调查提供了有关当前文献中普遍存在的交互式提问方法的详细概述。它首先要解释提问系统的基本原理,从而定义新的符号和分类法,以将所有已确定的作品结合在统一框架内。然后,根据提出的方法,评估方法和数据集/应用程序域来介绍和检查有关交互式问题解答系统的审查已发表的工作。我们还描述了围绕社区提出的特定任务和问题的趋势,从而阐明了学者的未来利益。 GitHub页面的综合综合了本文献研究中涵盖的所有主要主题,我们的工作得到了进一步的支持。 https://sisinflab.github.io/interactive-question-answering-systems-survey/
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近年来,新发现的矿物沉积物数量和不同矿物质需求的增加有LED探索地质学家,寻找在矿物勘探的每个阶段加工不同数据类型的更有效和创新的方法。作为主要步骤,诸如岩性单元,改变类型,结构和指示剂矿物的各种特征被映射以辅助靶向矿床的决策。不同类型的遥感数据集如卫星和空气传播数据,使得可以克服与映射地质特征相关的常见问题。从不同平台获得的遥感数据量的快速增加鼓励科学家培养先进,创新和强大的数据处理方法。机器学习方法可以帮助处理广泛的遥感数据集,并确定诸如反射连续体和感兴趣的特征的组件之间的关系。这些方法在处理频谱和地面真理测量中是稳健的,用于噪声和不确定性。近年来,通过补充与遥感数据集的地质调查进行了许多研究,现在在地球科学研究中突出。本文对一些流行的和最近建立的机器学习方法的实施和适应提供了全面的审查,用于处理不同类型的遥感数据,并调查其用于检测各种矿床类型的应用。我们展示了组合遥感数据和机器学习方法的高能力,以映射对于提供潜在地图至关重要的不同地质特征。此外,我们发现高级方法的范围来处理新一代遥感数据,以创建改进的矿物前景图。
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In the Metaverse, the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, efficient storage and data management and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. We then discuss challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.
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远程感知的地理空间数据对于包括精确农业,城市规划,灾害监测和反应以及气候变化研究等应用至关重要。对于在类似的计算机视觉任务中的深度神经网络的成功和可用的远程感测图像的纯粹体积的情况下,深入学习方法尤为前接受了许多遥感任务。然而,数据收集方法的方差和地理空间元数据的处理使得深度学习方法的应用成为远程感测的数据不动性。例如,卫星图像通常包括超出红色,绿色和蓝色的额外光谱频带,并且必须连接到可以具有不同坐标系,界限和分辨率的其他地理空间数据源。为了帮助实现遥感应用的深度学习的潜力,我们介绍了一个Pythono库的Torchgeo,用于将地理空间数据集成到Pytorch深度学习生态系统中。 Torchgeo为各种基准数据集,用于通用地理空间数据源的可组合数据集,用于地理空间数据的采样器以及使用多光谱图像的转换的数据加载器。 Torchgeo也是第一个为多光谱卫星图像提供预先训练的模型的库(例如,使用Sentinel 2卫星的所有频段的模型),允许在下游遥感任务上传输学习,其中包含有限的标记数据。我们使用Torchgeo在现有数据集上创建可重复的基准结果,并将我们的建议方法用于直通预处理地理空间图像。 Torchgeo是开源的,可在GitHub上提供:https://github.com/microsoft/torchgeo。
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