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Data Mining and Knowledge Discovery Tools for Exploiting Big Earth Observation Data

Espinoza Molina, Daniela and Datcu, Mihai (2015) Data Mining and Knowledge Discovery Tools for Exploiting Big Earth Observation Data. In: Proceedings of International Symposium on Remote Sensing of Environment (ISRSE) 2015, XL-7 (W3), pp. 627-633. Copernicus Publications. 36th International Symposium on Remote Sensing of Environment (ISRSE), 11.-15. Mai 2015, Berlin, Germany. DOI: 10.5194/isprsarchives-XL-7-W3-627-2015

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Official URL: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/627/2015/

Abstract

The continuous increase in the size of the archives and in the variety and complexity of Earth-Observation (EO) sensors require new methodologies and tools that allow the end-user to access a large image repository, to extract and to infer knowledge about the patterns hidden in the images, to retrieve dynamically a collection of relevant images, and to support the creation of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In this context, we are concerned with providing a platform for data mining and knowledge discovery content from EO archives. The platform’s goal is to implement a communication channel between Payload Ground Segments and the end-user who receives the content of the data coded in an understandable format associated with semantics that is ready for immediate exploitation. It will provide the user with automated tools to explore and understand the content of highly complex images archives. The challenge lies in the extraction of meaningful information and understanding observations of large extended areas, over long periods of time, with a broad variety of EO imaging sensors in synergy with other related measurements and data. The platform is composed of several components such as 1.) ingestion of EO images and related data providing basic features for image analysis, 2.) query engine based on metadata, semantics and image content, 3.) data mining and knowledge discovery tools for supporting the interpretation and understanding of image content, 4.) semantic definition of the image content via machine learning methods. All these components are integrated and supported by a relational database management system, ensuring the integrity and consistency of Terabytes of Earth Observation data.

Item URL in elib:https://elib.dlr.de/102179/
Document Type:Conference or Workshop Item (Speech)
Additional Information:© Author(s) 2015. This work is distributed under the Creative Commons Attribution 3.0 License.
Title:Data Mining and Knowledge Discovery Tools for Exploiting Big Earth Observation Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Espinoza Molina, Danieladaniela.espinozamolina (at) dlr.deUNSPECIFIED
Datcu, Mihaimihai.datcu (at) dlr.deUNSPECIFIED
Date:April 2015
Journal or Publication Title:Proceedings of International Symposium on Remote Sensing of Environment (ISRSE) 2015
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:XL-7
DOI :10.5194/isprsarchives-XL-7-W3-627-2015
Page Range:pp. 627-633
Editors:
EditorsEmail
Schreier, G.UNSPECIFIED
Skrovseth, P. E.UNSPECIFIED
Staudenrausch, H.UNSPECIFIED
Publisher:Copernicus Publications
Series Name:ISPRS Archive
Status:Published
Keywords:Systems to manage Earth-Observation images, data mining, knowledge discovery, query engines
Event Title:36th International Symposium on Remote Sensing of Environment (ISRSE)
Event Location:Berlin, Germany
Event Type:international Conference
Event Dates:11.-15. Mai 2015
Organizer:International Society for Photogrammetry and Remote Sensing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By:INVALID USER
Deposited On:14 Jan 2016 17:08
Last Modified:31 Jul 2019 19:59

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