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Spacecraft missions venturing beyond Earth rely upon ranging, specific payloads or supports systems, claiming the usage of facilities such as Deep Space Network or ESTRACK. This requires a significant amount of resources both on Earth and onboard, especially in monetary terms. Furthermore, satellite link is not always guaranteed, and results are not available in real-time. Therefore, to cruise independently of Earth-based operators and to achieve the requirements raised by the next planetary exploratory missions, this manuscript proposes a novel visual-based terrain relative navigation (TRN) system. TRN is promising because can be applied to a wide range of space missions, e.g. planetary exploration (rocky planets), the study of moons of gaseous planets, approach phase of comets, asteroid, and other celestial bodies. In essence, a spacecraft can retrieve its absolute position by matching a pattern of observed craters with a database. The measurements thus obtained can be integrated into a navigation filter to estimate the spacecraft state (position and velocity). The ability to detect match surface features to a map is crucial for TRN. However, craters largely vary their appearances also depending on image qualities, lighting geometry, and noises. For these reasons, realizing a crater detector able to generalize to different scenarios is complex. It is worth considering that this task must be performed in a robust way to keep high the navigation accuracy. In past, this has led to least square approaches, creating situations where corrupted navigation states render otherwise good images ineffectual, leading to unnecessary filter reinitializations, trajectory aborts (e.g. during lunar descent), or other undesirable events. Contrarily, the solution proposed is reliable, combining the strengths of a region-based convolutional neural network (Mask R-CNN) with the robustness of projective invariants theory. An extended Kalman filter completes the TRN system, further increasing the stability of the system. Despite the usage of medium resolution (118 m/px) data, results showed that the navigation accuracy lies below 400 meters in the best-case scenario for a satellite orbiting around the Moon at about 50 km altitude. This is expected to guarantee real-time autonomous onboard operations with no need for ground support.

Plant nursery industry makes extensive use of non-biodegradable plastic pots (polypropylene, PP); in Italy, it is estimated an annual consumption of about 440 million PP pots which, at the end of their use, represent an expensive waste to be disposed of; therefore, the replacement of PP pot with similar products based on biodegradable materials, may reduce significantly the environmental impact of this sector. ECO-POT project aims at a cost-effectively production and successive field validation of biodegradable pots realized in different sizes; the mechanical properties of pots, i.e. resistance to lateral deformation and crushing, and rate of biodegradation should match needs of both operators and consumers. In particular, the biodegradation will be tested on plantable pot which are left intact on the root ball and planted into the field. In this case pots are designed to allow root to grow through the container walls and to decompose rapidly after being planted. In addition, the rate of biodegradation will be also tested on pots which are designed to be removed before final planting, thus speculating on the compostability of the used containers (test ISO UNI 14855-1) Different commercial biodegradable products, alone or mixed, will be tested. ECOPOT project involves different plant nursery farms located in Pistoia; up to 10,000 ornamental plants, placed in pots of different sizes, in different environments and belonging to 10 different species / varieties, will be tested. The most important expected results are: reduction in the consumption of non-renewable resources; economic benefits for nurseries gained in the international markets thanks to the adoption of recognizable low environmental impact trademarks (ECOLAB).

The paper proposes the use of Cascade Mask R-CNN for the detection of craters from monocular images. Crater detection is a challenging task being the images prone to changes in lighting and noise conditions. Besides, the crater appearance is strongly modified according to the region of interest, being the shadows strongly affected by the sun vector inclination. To tackle these issues, the paper exploits the generalizability of modern deep learning architectures to create a highly reliable crater detector. The dataset used for transfer learning the model comprises more than 800 real lunar monocular images obtained from the lunar reconnaissance orbiter (LRO) cameras. Results confirm the performance reached by the multi-stage object detection architecture both in equatorial and polar regions, its robustness, and the validity of this crater detection scheme for planetary navigation tasks.

The recent launches of Earth Observation (EO) satellites have made numerous SAR images available to dynamically monitor the ocean with improved spatial resolution at shorter revisit time. Focusing on automatic target detection of ships with the specific aim of improving our Maritime Domain Awareness (MDA), this work assesses the capabilities of multi-frequency/multi-mission spaceborne Synthetic Aperture Radar (SAR) data. Specifically, Sentinel-1 (C-band), COSMO-SkyMed (X-band), and SAOCOM (L-band) missions have been considered in this analysis. The aim of the paper is to present an efficient approach for interpreting images acquired within small time gaps, ensuring fisheries and pollution control, anti-piracy actions, and surveillance over coastal/protected regions.

Maritime trade and trasport occupy a pivotal position in the current era of globalization. Thus, monitoring ships at sea represents the starting point of this paper in which a novel approach to detect ships by wake has been proposed, based on Instance Segmentation deep learning architecture Mask R-CNN. In order to train and test this network, 766 wake chips cropped from 50 multispectral images acquired from Sentinel-2 satellites were observed. In particular, B2 (blue), B3 (green), B4 (red) and B8 (Infrared) bands were considered since they are all characterized by same resolution. The results proved that Mask R-CNN is capable to detect the vast majority of ship wakes with high confidence percentage in different configurations, i.e. slanted wakes, multiple wake scenarios or wakes in dark areas not related to their features.

Advancements in Computer Vision (CV) and Machine Learning (ML) of past decades have contributed to the realization of autonomous systems like self-driving cars. This manuscript explores the possibility of transferring this technology to the next planetary exploratory missions. Similarly to a star tracker, it is possible to match a pattern of observed craters with a reference, i.e. a crater catalogue, in order to perform the spacecraft state estimation with no external support (i.e. GNSS or DSN). Such kind of technology, born for missilistic applications before the advent of GPS, is known as Terrain Relative Navigation (TRN). However, unlike stars, craters largely vary their appearances also depending on image qualities, lighting geometry and noises. While these problems can nowadays be overcome with the modern approach of deep learning, the inherent limit of crater detectors, i.e. the false detections, still poses a problem for the matching phase. In response, this paper proposes a novel solution, exploiting attitude and sensor pointing knowledge to discriminate false matches. A complete TRN system, called FederNet, was finally developed implementing the matching algorithm within a processing chain including a Convolutional Neural Network and an extended Kalman filter (EKF). FederNet has been validated with a numerical anlysis on real lunar elevation images. However, the adopted methodology further extends to other airless bodies. Despite the usage of a medium resolution (118 m/px) Digital Elevation Model (DEM), results showed that the navigation accuracy lie below 400 meters in the best case scenario, guaranteeing real time autonomous on-board operations with no need for ground support. The capabilities of such TRN system can be additionally improved with higher resolution data and data fusion integration with other sensor measurements.