Fan, Fan und Shi, Yilei und Zhu, Xiaoxiang (2024) Urban Land Cover Classification with Efficient Hybrid Quantum Machine Learning Model. IEEE CEC 2024, 2024-06-30 - 2024-07-05, Yokohama, Japan. (im Druck)
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Kurzfassung
Urban land cover classification aims to derive crucial information from earth observation data and categorize it into specific land uses. To achieve accurate classification, sophisticated machine learning models trained with large earth observation data are employed, but the required computation power has become a bottleneck. Quantum computing might tackle this challenge in the future. However, representing images into quantum states for analysis with quantum computing is challenging due to the high demand for quantum resources. To tackle this challenge, we propose a hybrid quantum neural network that can effectively represent and classify remote sensing imagery with reduced quantum resources. Our model was evaluated on the Local Climate Zone (LCZ)-based land cover classification task using the TensorFlow Quantum platform, and the experimental results indicate its validity for accurate urban land cover classification.
elib-URL des Eintrags: | https://elib.dlr.de/204201/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | Urban Land Cover Classification with Efficient Hybrid Quantum Machine Learning Model | ||||||||||||||||
Autoren: |
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Datum: | 2024 | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Nein | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
Status: | im Druck | ||||||||||||||||
Stichwörter: | Quantum Machine Learning, Quantum Image Encoding, Quantum Circuit, Urban Land Cover Classification | ||||||||||||||||
Veranstaltungstitel: | IEEE CEC 2024 | ||||||||||||||||
Veranstaltungsort: | Yokohama, Japan | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 30 Juni 2024 | ||||||||||||||||
Veranstaltungsende: | 5 Juli 2024 | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||
Hinterlegt von: | Fan, Fan | ||||||||||||||||
Hinterlegt am: | 16 Mai 2024 13:35 | ||||||||||||||||
Letzte Änderung: | 16 Mai 2024 13:35 |
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