Emerging trends in big Earth data management and analysis

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2023-07-03 DOI:10.1080/20964471.2023.2237829
M. Sudmanns, G. Giuliani, D. Tiede, H. Augustin
{"title":"Emerging trends in big Earth data management and analysis","authors":"M. Sudmanns, G. Giuliani, D. Tiede, H. Augustin","doi":"10.1080/20964471.2023.2237829","DOIUrl":null,"url":null,"abstract":"Big Earth data are increasingly used in a variety of applications. At the same time, technological developments happen rapidly and include Earth observation data cubes, analysis-ready data (ARD), the need to access distributed systems and data to avoid replicating datasets, searching and finding datasets, or visualization of data and information in a comprehensive way. The current pace in which technology and methodology using big Earth data is developed is high, but this should be seen as an opportunity to strive for flexible and innovative solutions. Those solutions and approaches may even come from other domains and disciplines as remote sensing or Earth observation (EO) in general and they should be embraced as a facilitator of the multiand interdisciplinary nature that is inherent to big Earth data science and research. Although worthwhile to envision, it would be a challenging or even impossible task to get a full and comprehensive overview over the state-of-the-art, developments, and current research agendas of all disciplines that are contributing to big Earth data. In this special issue, we aimed to curate contributions that can be seen as a snapshot of “emerging trends” instead of providing a comprehensive overview, which is hardly possible in such a highly dynamic field. Seven papers illustrate the variety of topics, different available solutions, and challenges that lie ahead. The contributions are as varied as the topics and range from technical notes as a state-of-the-art report to very detailed, comprising articles. The contributions can be categorised into four sub-topics: data sources, data management, data analytics, and data visualization. However, the boundaries of these categories cannot be strictly drawn, and investigations of individual solutions provided in the articles can be put into their contexts within a bigger big Earth data workflow. Baraldi et al. (2022a, 2022b) investigate the concept of ARD in two papers (part 1 and part 2), and proposes a new workflow for generating ARD. These papers are technologically dense, but provide concepts and ideas for quality indicators, while also considering data storage and querying. Considering that the quality of subsequent analyses depends largely on the quality of input data, providing high-quality ARD is of interest for the entire community. Backeberg et al. (2022) describe technical solutions for federated big Earth data management and processing. With the objective to overcome limitations of requirements to keep all data within one system, different providers may share responsibilities and technical implementations of concepts within the overall system. For users, such a system is aimed to be transparent and usable without technical barriers. Such federated systems BIG EARTH DATA 2023, VOL. 7, NO. 3, 451–454 https://doi.org/10.1080/20964471.2023.2237829","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"229 1","pages":"451 - 454"},"PeriodicalIF":4.2000,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Earth Data","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/20964471.2023.2237829","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Big Earth data are increasingly used in a variety of applications. At the same time, technological developments happen rapidly and include Earth observation data cubes, analysis-ready data (ARD), the need to access distributed systems and data to avoid replicating datasets, searching and finding datasets, or visualization of data and information in a comprehensive way. The current pace in which technology and methodology using big Earth data is developed is high, but this should be seen as an opportunity to strive for flexible and innovative solutions. Those solutions and approaches may even come from other domains and disciplines as remote sensing or Earth observation (EO) in general and they should be embraced as a facilitator of the multiand interdisciplinary nature that is inherent to big Earth data science and research. Although worthwhile to envision, it would be a challenging or even impossible task to get a full and comprehensive overview over the state-of-the-art, developments, and current research agendas of all disciplines that are contributing to big Earth data. In this special issue, we aimed to curate contributions that can be seen as a snapshot of “emerging trends” instead of providing a comprehensive overview, which is hardly possible in such a highly dynamic field. Seven papers illustrate the variety of topics, different available solutions, and challenges that lie ahead. The contributions are as varied as the topics and range from technical notes as a state-of-the-art report to very detailed, comprising articles. The contributions can be categorised into four sub-topics: data sources, data management, data analytics, and data visualization. However, the boundaries of these categories cannot be strictly drawn, and investigations of individual solutions provided in the articles can be put into their contexts within a bigger big Earth data workflow. Baraldi et al. (2022a, 2022b) investigate the concept of ARD in two papers (part 1 and part 2), and proposes a new workflow for generating ARD. These papers are technologically dense, but provide concepts and ideas for quality indicators, while also considering data storage and querying. Considering that the quality of subsequent analyses depends largely on the quality of input data, providing high-quality ARD is of interest for the entire community. Backeberg et al. (2022) describe technical solutions for federated big Earth data management and processing. With the objective to overcome limitations of requirements to keep all data within one system, different providers may share responsibilities and technical implementations of concepts within the overall system. For users, such a system is aimed to be transparent and usable without technical barriers. Such federated systems BIG EARTH DATA 2023, VOL. 7, NO. 3, 451–454 https://doi.org/10.1080/20964471.2023.2237829
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
大地球数据管理和分析的新趋势
大地球数据越来越多地用于各种应用。与此同时,技术发展迅速,包括地球观测数据立方体,分析就绪数据(ARD),需要访问分布式系统和数据以避免复制数据集,搜索和查找数据集,或以全面的方式将数据和信息可视化。目前使用地球大数据的技术和方法的发展速度很快,但这应被视为努力寻求灵活和创新解决方案的机会。这些解决方案和方法甚至可能来自其他领域和学科,如遥感或一般的地球观测(EO),它们应被视为地球大数据科学和研究所固有的多学科和跨学科性质的促进者。虽然值得设想,但要全面全面地了解对地球大数据有贡献的所有学科的最新技术、发展和当前研究议程,这将是一项具有挑战性甚至是不可能的任务。在这期特刊中,我们的目的是提供可以被视为“新兴趋势”的快照,而不是提供全面的概述,这在这样一个高度动态的领域几乎是不可能的。七篇论文阐述了各种主题、不同的可用解决方案和未来的挑战。贡献的内容和主题一样多样,从作为最新报告的技术说明到非常详细的文章。这些贡献可以分为四个子主题:数据源、数据管理、数据分析和数据可视化。然而,这些类别的边界不能严格划定,文章中提供的单个解决方案的调查可以放在更大的大地球数据工作流的上下文中。Baraldi等人(2022a, 2022b)在两篇论文(第1部分和第2部分)中研究了ARD的概念,并提出了一种新的ARD生成工作流程。这些论文技术密集,但提供了质量指标的概念和思路,同时也考虑了数据存储和查询。考虑到后续分析的质量在很大程度上取决于输入数据的质量,提供高质量的ARD对整个社区都很有意义。Backeberg等人(2022)描述了联邦大地球数据管理和处理的技术解决方案。为了克服将所有数据保存在一个系统内的需求限制,不同的提供者可以在整个系统内共享概念的责任和技术实现。对于用户来说,这样一个系统的目的是透明和可用,没有技术障碍。《大地球数据》,第7卷,第2023期。3,451 - 454 https://doi.org/10.1080/20964471.2023.2237829
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
期刊最新文献
A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data The first 10 m resolution thermokarst lake and pond dataset for the Lena Basin in the 2020 thawing season A high-resolution dataset for lower atmospheric process studies over the Tibetan Plateau from 1981 to 2020 An application of 1D convolution and deep learning to remote sensing modelling of Secchi depth in the northern Adriatic Sea A mediation system for continuous spatial queries on a unified schema using Apache Spark
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1