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Conference Paper/Proceeding/Abstract 414 views 124 downloads

Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions

Shruti Nair, Vasile Palade, Sara Sharifzadeh Orcid Logo, Charley Hill-Butler

2024 International Conference on Machine Learning and Applications (ICMLA), Pages: 1593 - 1598

Swansea University Author: Sara Sharifzadeh Orcid Logo

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DOI (Published version): 10.1109/icmla61862.2024.00246

Published in: 2024 International Conference on Machine Learning and Applications (ICMLA)
ISBN: 979-8-3503-7489-6 979-8-3503-7488-9
ISSN: 1946-0740 1946-0759
Published: IEEE 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa68669
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last_indexed 2025-03-18T05:29:12Z
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spelling 2025-03-17T11:26:49.0251002 v2 68669 2025-01-10 Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2025-01-10 MACS Conference Paper/Proceeding/Abstract 2024 International Conference on Machine Learning and Applications (ICMLA) 1593 1598 IEEE 979-8-3503-7489-6 979-8-3503-7488-9 1946-0740 1946-0759 Training, Time-frequency analysis, Adaptation models, Accuracy, Time series analysis, Crops, Transformers, Feature extraction, Monitoring, Remote sensing 18 12 2024 2024-12-18 10.1109/icmla61862.2024.00246 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required Global Challenge Research Fund (Coventry University) 2025-03-17T11:26:49.0251002 2025-01-10T11:42:14.2491370 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Shruti Nair 1 Vasile Palade 2 Sara Sharifzadeh 0000-0003-4621-2917 3 Charley Hill-Butler 4 68669__33731__817c46c1848d4bbdacf5c505bad1734e.pdf AcceptedVersion.pdf 2025-03-04T16:48:53.8816742 Output 385597 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions
spellingShingle Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions
Sara Sharifzadeh
title_short Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions
title_full Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions
title_fullStr Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions
title_full_unstemmed Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions
title_sort Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Shruti Nair
Vasile Palade
Sara Sharifzadeh
Charley Hill-Butler
format Conference Paper/Proceeding/Abstract
container_title 2024 International Conference on Machine Learning and Applications (ICMLA)
container_start_page 1593
publishDate 2024
institution Swansea University
isbn 979-8-3503-7489-6
979-8-3503-7488-9
issn 1946-0740
1946-0759
doi_str_mv 10.1109/icmla61862.2024.00246
publisher IEEE
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
published_date 2024-12-18T05:26:06Z
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