Urban Land Cover Classification With Efficient Hybrid Quantum Machine Learning Model

MCML Authors

Xiaoxiang Zhu

Prof. Dr.

Principal Investigator

Data Science in Earth Observation

Abstract

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.

inproceedings

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CEC 2024

IEEE Congress on Evolutionary Computation. Yokohama, Japan, Jun 30-Jul 05, 2024.

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Authors

F. Fan • Y. Shi • X. Zhu

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Links

DOI

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Research Area

 C3 | Physics and Geo Sciences