Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network
This work addresses computational challenges in Earth observation data analysis for researchers and practitioners, though it is incremental in applying quantum methods to this domain.
The paper tackled the computational bottleneck in analyzing large Earth observation data by proposing a hybrid quantum network with multitask learning for classification, achieving competitive results on multiple benchmarks.
Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) has entered the era of Big Data, and the computational demands for effectively analyzing large EO data with complex deep learning models have become a bottleneck. Motivated by this, we aim to leverage quantum computing for EO data classification and explore its advantages despite the current limitations of quantum devices. This paper presents a hybrid model that incorporates multitask learning to assist efficient data encoding and employs a location weight module with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple EO benchmarks. Additionally, we experimentally explored the generalizability of our model and investigated the factors contributing to its advantage, highlighting the potential of QML in EO data analysis.