Assessment of Spectral based Solutions for the Detection of Floating Marine Debris

Typically, the detection of marine debris relies on in-situ campaigns that are characterized by huge human effort and limited spatial coverage. Following the need of a rapid solution for the detection of floating plastic, methods based on remote sensing data have been proposed recently. Their main limitation is represented by the lack of a general reference for evaluating performance. Recently, the Marine Debris Archive (MARIDA) has been released as a standard dataset to develop and evaluate Machine Learning (ML) algorithms for detection of Marine Plastic Debris. The MARIDA dataset has been created for simplifying the comparison between detection solutions with the aim of stimulating the research in the field of marine environment preservation. In this work, an assessment of spectral based solutions is proposed by evaluating performance on MARIDA dataset. The outcome highlights the need of precise reference for fair evaluation.

Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks

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.