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Discovering Knowledge Hidden in Raster Images using RasterMiner

Published: 21 August 2021 Publication History

Abstract

The satellite imagery data naturally exists as raster data. Useful information that can empower the domain experts to improve their decision-making abilities lies hidden in this data. However, finding this hidden knowledge is non-trivial and challenging due to the lack of open source integrated software to discover knowledge from raster data. In particular, existing open-source general-purpose data mining libraries, such as Knime [1], Mahout [3], Weka [5], Sci-kit [4], and SPMF [2], are inadequate to find knowledge hidden in raster datasets.
In this talk, we present rasterMiner an integrated open-source software to discover knowledge from raster imagery datasets. It currently provides unsupervised learning techniques, such as pattern mining and clustering, to discover knowledge hidden in raster data. The key features of our software are as follows: (i) provides four pattern mining algorithms and four clustering algorithms to discover knowledge from raster data, (ii) Our software also provides "elbow method" to choose an appropriate k value for k-mean and k-means++ algorithms, (iii) Our software presents an integrated GUI that can facilitate the domain experts to choose algorithm(s) of their choice, (iv) Our software can also be accessed as a python-library, (v) The knowledge discovered by our software can be stored in standard formats so that the generated knowledge can be visualized using any GIS software.

References

[1]
Michael R. Berthold, Nicolas Cebron, Fabian Dill, Thomas R. Gabriel, Tobias Kotter, Thorsten Meinl, Peter Ohl, Kilian Thiel, and Bernd Wiswedel. 2009. KNIME - the Konstanz Information Miner: Version 2.0 and Beyond. SIGKDD Explor. Newsl. 11, 1 (Nov. 2009), 26--31. https://doi.org/10.1145/1656274.1656280
[2]
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Zhihong Deng, and Hoang Thanh Lam. 2016. The SPMF Open-Source Data Mining Library Version 2. In Machine Learning and Knowledge Discovery in Databases, Bettina Berendt, Bjorn Bringmann, Elisa Fromont, Gemma Garriga, Pauli Miettinen, Nikolaj Tatti, and Volker Tresp (Eds.). Springer International Publishing, Cham, 36--40.
[3]
Mahout 2017. Apache Mahout. Retrieved May 27, 2021 from https://mahout.apache.org/
[4]
Sci-kit [n.d.]. Scikit. Retrieved May 27, 2021 from https://scikit-learn.org/stable/
[5]
Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal. 2016. Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques (4th ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

Cited By

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  • (2024)Clustering-based compression for raster time seriesThe Computer Journal10.1093/comjnl/bxae09068:1(32-46)Online publication date: 29-Sep-2024

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  1. Discovering Knowledge Hidden in Raster Images using RasterMiner

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    cover image ACM Conferences
    ICDAR '21: Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval
    August 2021
    72 pages
    ISBN:9781450385299
    DOI:10.1145/3463944
    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.

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    Published: 21 August 2021

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    Author Tags

    1. clustering
    2. knowledge discovery in datasets
    3. pattern mining
    4. raster data

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    • Japan Society forthe Promotion of Science KAKENHI

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    • (2024)Clustering-based compression for raster time seriesThe Computer Journal10.1093/comjnl/bxae09068:1(32-46)Online publication date: 29-Sep-2024

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