Satellite-based remote sensing images are essential for Earth surface analysis, serving diverse purposes in both civilian and military domains. Satellite images are used for analysis and decision making and are considered a reliable source of information. Recently, the field of image generation has developed increasingly sophisticated techniques, such as generative neural models, usually known as Generative Adversarial Neural Networks (GAN), to create synthetic images from scratch that appear almost real. Generative models have traditionally been applied to RGB or grayscale images and have been used for generating fake images of faces, animals, or objects. Currently, there are few studies regarding the application of GAN to multispectral satellite images. This work aims to test GAN models against the generation of multispectral satellite images, and in particular, the work explores the ability of the state-of-the-art StyleGAN3 model to produce high-quality synthetic Sentinel-2 images. The work delves into the configuration, training process, and evaluation of StyleGAN3 using custom Sentinel-2 datasets. StyleGAN3 results are compared with those provided by the proGAN model, the only GAN model tested so far on multispectral satellite data. Evaluation methods include visual inspection, spectral signature analysis, and a modified Fréchet Inception Distance for quantitative assessment. Results show that StyleGAN3 outperforms proGAN model exhibiting visually pleasing images. The quantitative comparison shows that StyleGAN3 provides the best results in terms of FID scores, in particular the improvement compared to proGAN increases as the spatial extent and spectral dimension of the generated images increases.

The rapid growth of hyperspectral satellite missions and the subsequent availability of hyperspectral images have encouraged the remote sensing community to investigate their potential in estimating the concentration of gases, as CH ₄ and CO ₂ , which are related to the greenhouse effect. Though satellite hyperspectral sensors are not specifically designed for this purpose, they are expected to complement more specific satellite missions, such as NASA’s OCO-2 and OCO-3, both in terms of enriched temporal sampling and improved spatial resolution. In this work, we present a new method to estimate the column-averaged dry-air mole fraction of CO ₂ from hyperspectral data on a per-pixel basis. The method, which is here tailored to PRISMA images, leverages the spectral radiance samples collected in the SWIR spectral region around the CO ₂ absorption band at 2000 nm. By assuming a linear model to describe the dependence of the observed radiance on the CO ₂ concentration, the estimation problem is reduced to matched filtering and can be effectively implemented in compliance with the low computational burden required to perform a pixel by pixel analysis. The performance of the presented method is investigated by means of a rigorous, physically based simulator that accurately reproduces the at-sensor radiance allowing one to check the validity of the assumptions and to assess the algorithm accuracy. The results show that the presented algorithm outperforms a benchmark CIBR based approach, which has been proposed in the literature to get fast per-pixel estimates of CO ₂ concentration.

There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3D marine habitat mapping, require that a number of heterogeneous vehicles operate simultaneously in small areas, often in close proximity of each other. In these circumstances safety, reliability, and efficient multiple vehicle operation are key ingredients for mission success. Additionally, the deployment and operation of multiple AMVs at sea are extremely costly in terms of the logistics and human resources required for mission supervision, often during extended periods of time. These costs can be greatly minimized by automating the deployment and initial steering of a vehicle fleet to a predetermined configuration, in preparation for the ensuing mission, taking into account operational constraints. This is one of the core issues addressed in the scope of the Widely Scalable Mobile Underwater Sonar Technology project (WiMUST), an EU Horizon 2020 initiative for underwater robotics research.WiMUST uses a team of cooperative autonomous marine robots, some of which towing streamers equipped with hydrophones, acting as intelligent sensing and communicating nodes of a reconfigurable moving acoustic network. In WiMUST, the AMVs maintain a fixed geometric formation through cooperative navigation and motion control. Formation initialization requires that all the AMVs start from scattered positions in the water and maneuver so as to arrive at required target configuration points at the same time in a completely automatic manner. This paper describes the decoupled prioritized vehicle motion planner developed in the scope of WiMUST that, together with an existing system for trajectory tracking, affords a fleet of vehicles the above capabilities, while ensuring inter-vehicle collision and streamer entanglement avoidance. Tests with a fleet of seven marine vehicles show the efficacy of the system planner developed.

Hyperspectral (HS) satellite data are of considerable importance for applications such as environmental monitoring and precision agriculture, given the richness of the spectral information they contain. However, hyperspectral data typically exhibit limited spatial resolution and are less readily available than multispectral (MS) data. This study, which aims to simulate data with high spectral and spatial resolution, explores the use of attention-based spectral reconstruction (SR) techniques, specifically MST++, MIRNet, AWAN and Restormer, to derive HS data in the Visible Near InfraRed (VNIR) and Short-Wave InfraRed (SWIR) from MS imagery. High-resolution MS and HS image pairs are generated from AVIRIS-NG aerial data and employed for training procedures, thereby enabling the reconstruction of HS data that closely resembles the original measurements. The results indicate that SR techniques can considerably enhance the utility of existing MS datasets for HS-dependent applications. Such techniques can effectively be employed to synthesize high-resolution HS data from MS inputs, thereby facilitating the potential for developing a comprehensive end-to-end sensor simulator. This is particularly advantageous in the context of simulating data from a mission that has not yet become operational, as exemplified by the PRISMA-2G data, which could be simulated, for example, from Sentinel-2 data.

This paper proposes a novel, hybrid vision-based system to autonomously detect and track a specific moving target, in our case an evader UAV (Unmanned Aerial Vehicles), with a moving camera. The framework is based on a detection stage which exploits a Faster Region-based Convolutional Neural Network (Faster R-CNN) designed to detect the Region Of Interest (ROI) associated to the UAV's position in the image plane. The moving target is tracked by using an Optical Flow-based tracking system and a Kalman Filter is used to give temporal consistency between consecutive measurements. The tracking system is designed to be able to achieve real-time image processing on embedded systems, for this reason a lag compensation algorithm for the delay due to the Faster R-CNN computation time is implemented. Algorithm's performance is evaluated by computing the error between the true UAV position in the image plane and the estimated position resulting from the tracking system.

Agroforestry, an integrated land-use practice combining trees and woody shrubs with crop and animal farming, offers significant ecological and agricultural benefits, including enhanced biodiversity, improved soil fertility, and increased resilience to environmental pressures. Despite its advantages, agroforestry faces challenges such as high initial investments, long maturation periods for trees, land tenure issues and a high level of complexity in technical management. Digital agriculture introduces advanced technologies and sensors, which provide precise data on soil moisture, nutrient levels, and plant health, enabling more efficient resource use and better farm management. Integrating these sensing technologies into agroforestry can address key challenges, optimize irrigation and nutrient management, and enhance overall system productivity and sustainability. This review explores the interaction between agroforestry and digitalization, highlighting case studies, and discusses the potential for these technologies to support sustainable agriculture and climate change mitigation. Increased investment in research and development, along with supportive policies, is essential for advancing the adoption of these innovative practices in agroforestry.