  A significant amount of remotely sensed data is generated daily by many Earth
observation (EO) spaceborne and airborne sensors over different countries of
our planet. Different applications use those data, such as natural hazard
monitoring, global climate change, urban planning, and more. Many challenges
are brought by the use of these big data in the context of remote sensing
applications. In recent years, employment of machine learning (ML) and deep
learning (DL)-based algorithms have allowed a more efficient use of these data
but the issues in managing, processing, and efficiently exploiting them have
even increased since classical computers have reached their limits. This
article highlights a significant shift towards leveraging quantum computing
techniques in processing large volumes of remote sensing data. The proposed
Quanv4EO model introduces a quanvolution method for preprocessing
multi-dimensional EO data. First its effectiveness is demonstrated through
image classification tasks on MNIST and Fashion MNIST datasets, and later on,
its capabilities on remote sensing image classification and filtering are
shown. Key findings suggest that the proposed model not only maintains high
precision in image classification but also shows improvements of around 5\% in
EO use cases compared to classical approaches. Moreover, the proposed framework
stands out for its reduced parameter size and the absence of training quantum
kernels, enabling better scalability for processing massive datasets. These
advancements underscore the promising potential of quantum computing in
addressing the limitations of classical algorithms in remote sensing
applications, offering a more efficient and effective alternative for image
data classification and analysis.
