Pub Date : 2023-07-03DOI: 10.1080/20964471.2023.2230714
Lin Huang, Y. Qiu, Yang Li, Shuwen Yu, Wanyang Zhong, Changyong Dou
{"title":"DynIceData: a gridded ice–water classification dataset at short-time intervals based on observations from multiple satellites over the marginal ice zone","authors":"Lin Huang, Y. Qiu, Yang Li, Shuwen Yu, Wanyang Zhong, Changyong Dou","doi":"10.1080/20964471.2023.2230714","DOIUrl":"https://doi.org/10.1080/20964471.2023.2230714","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"33 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72860013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-03DOI: 10.1080/20964471.2023.2237829
M. Sudmanns, G. Giuliani, D. Tiede, H. Augustin
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
{"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":"https://doi.org/10.1080/20964471.2023.2237829","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":4.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74906358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-19DOI: 10.1080/20964471.2023.2222945
Luying Zhu, H. Tian, N. Huang, Li Wang, Z. Niu
{"title":"Spatial-temporal variations of surface water area during 1986–2018 in Qinghai Province, northwestern China based on Google Earth Engine","authors":"Luying Zhu, H. Tian, N. Huang, Li Wang, Z. Niu","doi":"10.1080/20964471.2023.2222945","DOIUrl":"https://doi.org/10.1080/20964471.2023.2222945","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"94 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80656177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-16DOI: 10.1080/20964471.2023.2222940
Wan-Feng Chen, Qi Zhang, J. Yang, Er-Teng Wang, Gaorui Song, Shoutao Jiao, Jie Yuan
{"title":"Trace element tectonic discrimination of granitoids: inspiration from big data analytics","authors":"Wan-Feng Chen, Qi Zhang, J. Yang, Er-Teng Wang, Gaorui Song, Shoutao Jiao, Jie Yuan","doi":"10.1080/20964471.2023.2222940","DOIUrl":"https://doi.org/10.1080/20964471.2023.2222940","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"12 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84883107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-04DOI: 10.1080/20964471.2023.2217576
Yue Han, Zhihua Zhang, M. Crabbe
{"title":"Generating high-resolution climate maps from sparse and irregular observations using a novel hybrid RBF network","authors":"Yue Han, Zhihua Zhang, M. Crabbe","doi":"10.1080/20964471.2023.2217576","DOIUrl":"https://doi.org/10.1080/20964471.2023.2217576","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"4 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85350762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-04DOI: 10.1080/20964471.2023.2196830
S. Eltazarov, I. Bobojonov, L. Kuhn, T. Glauben
{"title":"Improving risk reduction potential of weather index insurance by spatially downscaling gridded climate data - a machine learning approach","authors":"S. Eltazarov, I. Bobojonov, L. Kuhn, T. Glauben","doi":"10.1080/20964471.2023.2196830","DOIUrl":"https://doi.org/10.1080/20964471.2023.2196830","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"5 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84678904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-31DOI: 10.1080/20964471.2023.2195468
D. Martins, Miguel Ferreira, João Nuno Silva
ABSTRACT Global meteorology data are now widely used in various areas, but one of its applications, weather analogues, still require exhaustive searches on the whole historical data. We present two optimisations for the state-of-the-art weather analogue search algorithms: a parallelization and a heuristic search. The heuristic search (NDRank) limits of the final number of results and does initial searches on a lower resolution dataset to find candidates that, in the second phase, are locally validated. These optimisations were deployed in the Cloud and evaluated with ERA5 data from ECMWF. The proposed parallelization attained speedups close to optimal, and NDRank attains speedups higher than 4. NDRank can be applied to any parallel search, adding similar speedups. A substantial number of executions returned a set of analogues similar to the existing exhaustive search and most of the remaining results presented a numerical value difference lower than 0.1%. The results demonstrate that it is now possible to search for weather analogues in a faster way (even compared with parallel searches) with results with little to no error. Furthermore, NDRank can be applied to existing exhaustive searches, providing faster results with small reduction of the precision of the results.
{"title":"NDRank: optimised parallel search for weather analogues","authors":"D. Martins, Miguel Ferreira, João Nuno Silva","doi":"10.1080/20964471.2023.2195468","DOIUrl":"https://doi.org/10.1080/20964471.2023.2195468","url":null,"abstract":"ABSTRACT Global meteorology data are now widely used in various areas, but one of its applications, weather analogues, still require exhaustive searches on the whole historical data. We present two optimisations for the state-of-the-art weather analogue search algorithms: a parallelization and a heuristic search. The heuristic search (NDRank) limits of the final number of results and does initial searches on a lower resolution dataset to find candidates that, in the second phase, are locally validated. These optimisations were deployed in the Cloud and evaluated with ERA5 data from ECMWF. The proposed parallelization attained speedups close to optimal, and NDRank attains speedups higher than 4. NDRank can be applied to any parallel search, adding similar speedups. A substantial number of executions returned a set of analogues similar to the existing exhaustive search and most of the remaining results presented a numerical value difference lower than 0.1%. The results demonstrate that it is now possible to search for weather analogues in a faster way (even compared with parallel searches) with results with little to no error. Furthermore, NDRank can be applied to existing exhaustive searches, providing faster results with small reduction of the precision of the results.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"55 1","pages":"276 - 297"},"PeriodicalIF":4.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75647947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-27DOI: 10.1080/20964471.2023.2193281
Meredith L. Gore, Rowan Hilend, Jonathan O. Prell, E. Griffin, J. R. Macdonald, B. Keskin, Aaron Ferber, B. Dilkina
ABSTRACT Wildlife trafficking is a global phenomenon posing many negative impacts on socio-environmental systems. Scientific exploration of wildlife trafficking trends and the impact of interventions is significantly encumbered by a suite of data reuse challenges. We describe a novel, open-access data directory on wildlife trafficking and a corresponding visualization tool that can be used to identify data for multiple purposes, such as exploring wildlife trafficking hotspots and convergence points with other crime, discovering key drivers or deterrents of wildlife trafficking, and uncovering structural patterns. Keyword searches, expert elicitation, and peer-reviewed publications were used to search for extant sources used by industry and non-profit organizations, as well as those leveraged to publish academic research articles. The open-access data directory is designed to be a living document and searchable according to multiple measures. The directory can be instrumental in the data-driven analysis of unsustainable illegal wildlife trade, supply chain structure via link prediction models, the value of demand and supply reduction initiatives via multi-item knapsack problems, or trafficking behavior and transportation choices via network interdiction problems.
{"title":"A data directory to facilitate investigations on worldwide wildlife trafficking","authors":"Meredith L. Gore, Rowan Hilend, Jonathan O. Prell, E. Griffin, J. R. Macdonald, B. Keskin, Aaron Ferber, B. Dilkina","doi":"10.1080/20964471.2023.2193281","DOIUrl":"https://doi.org/10.1080/20964471.2023.2193281","url":null,"abstract":"ABSTRACT Wildlife trafficking is a global phenomenon posing many negative impacts on socio-environmental systems. Scientific exploration of wildlife trafficking trends and the impact of interventions is significantly encumbered by a suite of data reuse challenges. We describe a novel, open-access data directory on wildlife trafficking and a corresponding visualization tool that can be used to identify data for multiple purposes, such as exploring wildlife trafficking hotspots and convergence points with other crime, discovering key drivers or deterrents of wildlife trafficking, and uncovering structural patterns. Keyword searches, expert elicitation, and peer-reviewed publications were used to search for extant sources used by industry and non-profit organizations, as well as those leveraged to publish academic research articles. The open-access data directory is designed to be a living document and searchable according to multiple measures. The directory can be instrumental in the data-driven analysis of unsustainable illegal wildlife trade, supply chain structure via link prediction models, the value of demand and supply reduction initiatives via multi-item knapsack problems, or trafficking behavior and transportation choices via network interdiction problems.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"62 1","pages":"338 - 348"},"PeriodicalIF":4.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77597325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-20DOI: 10.1080/20964471.2023.2187659
R. Roncella, B. Ventura, Andrea Vianello, E. Boldrini, M. Santoro, P. Mazzetti, S. Nativi
ABSTRACT This paper is the third of a series that introduces some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform is a brokering infrastructure that brokers more than 190 autonomous information systems and data catalogs; it was created to provide the technological tool to implement the Global Earth Observation System of Systems (GEOSS). This manuscript focuses on the analysis of Eurac Research datasets and illustrates the data publishing process to enroll the Eurac Research Data Provider to the GEOSS Platform through the administrative and technical registrations. The study provides an analysis of the GEOSS user searches for Eurac Research data in order to understand the main use of datasets of an important Data Provider.
{"title":"Publishing Eurac Research data on the GEOSS Platform","authors":"R. Roncella, B. Ventura, Andrea Vianello, E. Boldrini, M. Santoro, P. Mazzetti, S. Nativi","doi":"10.1080/20964471.2023.2187659","DOIUrl":"https://doi.org/10.1080/20964471.2023.2187659","url":null,"abstract":"ABSTRACT This paper is the third of a series that introduces some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform is a brokering infrastructure that brokers more than 190 autonomous information systems and data catalogs; it was created to provide the technological tool to implement the Global Earth Observation System of Systems (GEOSS). This manuscript focuses on the analysis of Eurac Research datasets and illustrates the data publishing process to enroll the Eurac Research Data Provider to the GEOSS Platform through the administrative and technical registrations. The study provides an analysis of the GEOSS user searches for Eurac Research data in order to understand the main use of datasets of an important Data Provider.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"38 1","pages":"428 - 450"},"PeriodicalIF":4.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87276277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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