Cited By
View all- Wang LYan JMu LHuang L(2020)Knowledge discovery from remote sensing images: A reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.137110:5Online publication date: 22-May-2020
From Big Data to Big Information and Big Knowledge: the Case of Earth Observation Data
Authors: 
Konstantina Bereta, Manolis Koubarakis, Stefan Manegold, George Stamoulis, Begüm DemirAuthors Info & Claims
Konstantina Bereta, Manolis Koubarakis, Stefan Manegold, George Stamoulis, Begüm DemirAuthors Info & ClaimsPages 2293 - 2294
Abstract
Some particularly important rich sources of open and free big geospatial data are the Earth observation (EO) programs of various countries such as the Landsat program of the US and the Copernicus programme of the European Union. EO data is a paradigmatic case of big data and the same is true for the big information and big knowledge extracted from it. EO data (satellite images and in-situ data), and the information and knowledge extracted from it, can be utilized in many applications with financial and environmental impact in areas such as emergency management, climate change, agriculture and security.
References
[1]
Manolis Koubarakis, Konstantina Bereta, George Papadakis, Dimitrianos Savva, and George Stamoulis. 2017. Big, Linked Geospatial Data and Its Applications in Earth Observation. IEEE Internet Computing, Vol. July/August (2017), 87--91.
[2]
Manolis Koubarakis, Kostis Kyzirakos, Charalampos Nikolaou, George Garbis, Konstantina Bereta, Roi Doganinad Stella Giannakopoulou, Panayiotis Smeros, Dimitrianos Savva, George Stamoulis, Giannis Vlachopoulos, Stefan Manegold, Charalampos Kontoes, Themistocles Herekakis, Ioannis Papoutsis, and Dimitrios Michail. 2016. Managing Big, Linked, and Open Earth-Observation Data: Using the TELEIOS/LEO software stack. IEEE Geoscience and Remote Sensing Magazine, Vol. 4, 3 (2016), 23--37.
Index Terms
- From Big Data to Big Information and Big Knowledge: the Case of Earth Observation Data
Recommendations
From Copernicus Big Data to Big Information and Big Knowledge: A Demo from the Copernicus App Lab Project
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge ManagementCopernicus is the European program for monitoring the Earth. It consists of a set of complex systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of ...
Big Data in Climate: Opportunities and Challenges for Machine Learning
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningThe climate and Earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, massive amount of data about Earth and its environment is now continuously being generated by a large number of ...
Comments
Information & Contributors
Information
Published In
October 2018
2362 pages
ISBN:9781450360142
DOI:10.1145/3269206
- General Chair:
- Alfredo Cuzzocrea,
- Program Chairs:
- James Allan,
- Norman Paton,
- Divesh Srivastava,
- Rakesh Agrawal,
- Andrei Broder,
- Mohammed Zaki,
- Selcuk Candan,
- Alexandros Labrinidis,
- Assaf Schuster,
- Haixun Wang
Copyright © 2018 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 October 2018
Check for updates
Author Tags
Qualifiers
- Tutorial
Funding Sources
- European Commission
- European Commision - European Research Council
Conference
CIKM '18: The 27th ACM International Conference on Information and Knowledge Management
October 22 - 26, 2018
Torino, Italy
Acceptance Rates
CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 212Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)1
Reflects downloads up to 23 Dec 2024
Other Metrics
Citations
Cited By
View all- Wang LYan JMu LHuang L(2020)Knowledge discovery from remote sensing images: A reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.137110:5Online publication date: 22-May-2020
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in