rasterMiner: An Open-Source Python Library to Discover Knowledge From Raster Imagery Data
This paper introduces "rasterMiner," a comprehensive open-source Python software for extracting insights from satellite imagery data. This software offers 30 knowledge discovery algorithms spanning supervised and unsupervised techniques like classification, clustering, pattern mining, imag...
        Saved in:
      
    
          | Published in | 2024 IEEE Space, Aerospace and Defence Conference (SPACE) pp. 1160 - 1163 | 
|---|---|
| Main Authors | , , , | 
| Format | Conference Proceeding | 
| Language | English | 
| Published | 
            IEEE
    
        22.07.2024
     | 
| Subjects | |
| Online Access | Get full text | 
| DOI | 10.1109/SPACE63117.2024.10667674 | 
Cover
| Abstract | This paper introduces "rasterMiner," a comprehensive open-source Python software for extracting insights from satellite imagery data. This software offers 30 knowledge discovery algorithms spanning supervised and unsupervised techniques like classification, clustering, pattern mining, image fusion, and imputation. Notable attributes encompass an intuitive GUI for seamless algorithm selection, the adaptability of being accessed as a Python library, and the ability to export findings to standard CSV files for visualization in GIS software. Our software is bolstered by extensive support resources, including user and developer guides and a robust bug-reporting system. | 
    
|---|---|
| AbstractList | This paper introduces "rasterMiner," a comprehensive open-source Python software for extracting insights from satellite imagery data. This software offers 30 knowledge discovery algorithms spanning supervised and unsupervised techniques like classification, clustering, pattern mining, image fusion, and imputation. Notable attributes encompass an intuitive GUI for seamless algorithm selection, the adaptability of being accessed as a Python library, and the ability to export findings to standard CSV files for visualization in GIS software. Our software is bolstered by extensive support resources, including user and developer guides and a robust bug-reporting system. | 
    
| Author | Makiko, Ohtake Kiran, Rage Uday Veena, Pamalla Yoshiko, Ogawa  | 
    
| Author_xml | – sequence: 1 givenname: Pamalla surname: Veena fullname: Veena, Pamalla email: rage.vinny@gmail.com organization: The University of Aizu,Aizu-Wakamatsu,Japan – sequence: 2 givenname: Rage Uday surname: Kiran fullname: Kiran, Rage Uday email: udayrage@u-aizu.ac.jp organization: The University of Aizu,Aizu-Wakamatsu,Japan – sequence: 3 givenname: Ogawa surname: Yoshiko fullname: Yoshiko, Ogawa email: yoshiko@u-aizu.ac.jp organization: The University of Aizu,Aizu-Wakamatsu,Japan – sequence: 4 givenname: Ohtake surname: Makiko fullname: Makiko, Ohtake email: makiko-o@u-aizu.ac.jp organization: The University of Aizu,Aizu-Wakamatsu,Japan  | 
    
| BookMark | eNo1j8tOwzAUBY0ECyj9Axb-gYTrOPGDXZS2UFHUisKCVeUk18VSY1dOAPXviXiszmY0mnNFzn3wSAhlkDIG-na7Kau54IzJNIMsTxkIIYXMz8hUS614AVxIrsQleYumHzA-OY_xjpaero_ok234iA3SzWl4D56uXB1NPNEh0Jnrm_CJkT768HXAdo90EUNHn38sdNmZPY7kzAzmmlxYc-hx-rcT8rqYv1QPyWp9v6zKVeKYFEMirVGFVqCaNtdZUygoOLNg-ViY1bU11tSA3Na2bVsYwyVT2OhCMcisRs4n5ObX6xBxd4yuG1t3_4_5Nx4VURE | 
    
| ContentType | Conference Proceeding | 
    
| DBID | 6IE 6IL CBEJK RIE RIL  | 
    
| DOI | 10.1109/SPACE63117.2024.10667674 | 
    
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present  | 
    
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher  | 
    
| DeliveryMethod | fulltext_linktorsrc | 
    
| EISBN | 9798350367386 | 
    
| EndPage | 1163 | 
    
| ExternalDocumentID | 10667674 | 
    
| Genre | orig-research | 
    
| GroupedDBID | 6IE 6IL CBEJK RIE RIL  | 
    
| ID | FETCH-LOGICAL-i176t-7fa859808cd492c580531f0f33672bbfafab0e3fbfddd0503718ec958102f9e33 | 
    
| IEDL.DBID | RIE | 
    
| IngestDate | Wed Sep 18 05:50:19 EDT 2024 | 
    
| IsPeerReviewed | false | 
    
| IsScholarly | false | 
    
| Language | English | 
    
| LinkModel | DirectLink | 
    
| MergedId | FETCHMERGED-LOGICAL-i176t-7fa859808cd492c580531f0f33672bbfafab0e3fbfddd0503718ec958102f9e33 | 
    
| PageCount | 4 | 
    
| ParticipantIDs | ieee_primary_10667674 | 
    
| PublicationCentury | 2000 | 
    
| PublicationDate | 2024-July-22 | 
    
| PublicationDateYYYYMMDD | 2024-07-22 | 
    
| PublicationDate_xml | – month: 07 year: 2024 text: 2024-July-22 day: 22  | 
    
| PublicationDecade | 2020 | 
    
| PublicationTitle | 2024 IEEE Space, Aerospace and Defence Conference (SPACE) | 
    
| PublicationTitleAbbrev | SPACE | 
    
| PublicationYear | 2024 | 
    
| Publisher | IEEE | 
    
| Publisher_xml | – name: IEEE | 
    
| Score | 1.8800603 | 
    
| Snippet | This paper introduces "rasterMiner," a comprehensive open-source Python software for extracting insights from satellite imagery data. This software offers 30... | 
    
| SourceID | ieee | 
    
| SourceType | Publisher | 
    
| StartPage | 1160 | 
    
| SubjectTerms | artificial intelligence Big data Classification algorithms Clustering algorithms Knowledge discovery Libraries machine learning open-source pattern mining raster data Satellite images Software Software algorithms space  | 
    
| Title | rasterMiner: An Open-Source Python Library to Discover Knowledge From Raster Imagery Data | 
    
| URI | https://ieeexplore.ieee.org/document/10667674 | 
    
| hasFullText | 1 | 
    
| inHoldings | 1 | 
    
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LSwMxEA62J08qVnyTg9esu5vneit9UJWWYi3UU0myCRTprtTtof56d7ZdRUHwFkJIQoZhksn3fYPQjReh0soYEnFrCItSQbRmCWHCcC5lYqgDcvJwJAZT9jDjsx1ZveLCOOcq8JkLoFn95ae5XUOqrPRwQGRK1kANqcSWrFWjc8LkdjJud3qCRpEs330xC-rhPwqnVHGjf4BG9YpbuMhrsC5MYD9-iTH-e0uHqPVN0cPjr-BzhPZcdoxeVhp0D4ZA6LvD7QwDXIRMqvw8Hm9AJgDviAq4yHF38W4BwYkf68Qa7q_yJX6qZsH3S9C32OCuLnQLTfu9586A7GonkEUkRUGk14onKoTaRElsuQJn86GnVMjYGK-9NqGj3vg0TUETpoxRziZclRcOnzhKT1AzyzN3irCKKNPwASecZra0bCottyDtGEoZMXuGWnAu87etPMa8PpLzP_ov0D6YBxKkcXyJmsVq7a7KyF6Y68qin6QIpJc | 
    
| linkProvider | IEEE | 
    
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LSwMxEA5aD3pSseLbHLym7m5eu95KH1T7oNgW6qkk2QSKdFfq9lB_vTvbrqIgeAuBPMgwTDL5vm8QunPCC1WoNfG50YT5sSBKsYgwoTmXMtLUAjm5PxCdCXua8umWrF5wYay1BfjM1qBZ_OXHqVlBqiz3cEBkSraL9jhjjG_oWiU-x4vuR8N6oyWo78v85RewWjngR-mUInK0D9GgXHMDGHmtrTJdMx-_5Bj_vakjVP0m6eHhV_g5Rjs2OUEvSwXKB32g9D3geoIBMEJGRYYeD9cgFIC3VAWcpbg5fzeA4cTdMrWG28t0gZ-LWfDjAhQu1ripMlVFk3Zr3OiQbfUEMvelyIh0KuRR6EF1oigwPAR3c56jVMhAa6ec0p6lTrs4jkEVJo9S1kQ8zK8cLrKUnqJKkib2DOHQp0zBF5ywipnctrE03IC4oyelz8w5qsK5zN42Ahmz8kgu_ui_Rfudcb836z0OupfoAEwF6dIguEKVbLmy13mcz_RNYd1PchKn5A | 
    
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2024+IEEE+Space%2C+Aerospace+and+Defence+Conference+%28SPACE%29&rft.atitle=rasterMiner%3A+An+Open-Source+Python+Library+to+Discover+Knowledge+From+Raster+Imagery+Data&rft.au=Veena%2C+Pamalla&rft.au=Kiran%2C+Rage+Uday&rft.au=Yoshiko%2C+Ogawa&rft.au=Makiko%2C+Ohtake&rft.date=2024-07-22&rft.pub=IEEE&rft.spage=1160&rft.epage=1163&rft_id=info:doi/10.1109%2FSPACE63117.2024.10667674&rft.externalDocID=10667674 |