AGORA-EO: A unified ecosystem for earthobservation
| dc.contributor.author | Wall, Arne de | |
| dc.contributor.author | Deiseroth, Björn | |
| dc.contributor.author | Zacharatou, Eleni Tzirita | |
| dc.contributor.author | Quiané-Ruiz, Jorge-Arnulfo | |
| dc.contributor.author | Demir, Begüm | |
| dc.contributor.author | Markl, Volker | |
| dc.date.accessioned | 2022-11-15T12:13:47Z | |
| dc.date.available | 2022-11-15T12:13:47Z | |
| dc.date.issued | 2021-05-17 | |
| dc.description.abstract | Today’s EO exploitation platforms are limited to the processing functionalities and datasets they provide, and there is no single platform that provides all datasets of interest. Thus, it is crucial to enable cross-platform (federated) analytics to make EO technology easily accessible to everyone. We envision AgoraEO, an EO ecosystem for sharing, finding, composing, and executing EO assets, such as datasets, algorithms, and tools. Making AgoraEO a reality is challenging for several reasons, the main ones being that the ecosystem must provide interactive response times and operate seamlessly over multiple exploitation platforms. In this paper, we discuss the different challenges that AgoraEO poses as well as our ideas to tackle them. We believe that having an open, unified EO ecosystem would foster innovation and boost EO data literacy for the entire population. | en |
| dc.description.sponsorship | BMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Data | |
| dc.description.sponsorship | BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Data | |
| dc.identifier.isbn | 978-92-76-37661-3 | |
| dc.identifier.issn | 1831-9424 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/17670 | |
| dc.identifier.uri | https://doi.org/10.14279/depositonce-16455 | |
| dc.language.iso | en | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.ddc | 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten | de |
| dc.subject.other | Big Data | en |
| dc.subject.other | earth observation ecosystem | en |
| dc.subject.other | federated analytics | en |
| dc.title | AGORA-EO: A unified ecosystem for earthobservation | en |
| dc.title.subtitle | A vision for boosting EO data literacy | en |
| dc.type | Conference Object | |
| dc.type.version | publishedVersion | |
| dcterms.bibliographicCitation.doi | 10.2760/125905 | |
| dcterms.bibliographicCitation.pageend | 104 | |
| dcterms.bibliographicCitation.pagestart | 101 | |
| dcterms.bibliographicCitation.proceedingstitle | Proceedings of the 2021 conference on Big Data from Space | |
| tub.accessrights.dnb | free | |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Remote Sensing Image Analysis Group | |
| tub.publisher.universityorinstitution | Technische Universität Berlin |