We present a proof-of-concept onboard cloud detection system for the PRISMA Second Generation (PSG) mission, which combines a secondary forward-looking RGB camera with deep learning (DL) models on a system-on-a-chip (SoC) field-programmable gate array (FPGA). The proposed system enables real-time cloud coverage assessment, optimizing primary hyperspectral payload data collection and supporting adaptive acquisition scheduling, offering an effective solution for enhancing onboard data processing in Earth Observation (EO) missions. To support efficient model development, we derived the design specifications of the secondary camera and constructed a dedicated dataset using astronaut-captured images from the International Space Station – the CloudISS-RGB dataset – employing a self-training approach to generate high-quality pseudo-labels. Through an extensive architectural design exploration and systematic optimization, we tested two fully convolutional networks: U-Net, offering higher segmentation accuracy, and a lightweight convolutional autoencoder (CAE) designed for lower latency inference. The models were deployed on an AMD/Xilinx Zynq UltraScale+ MPSoC using the deep learning processing unit (DPU) IP core for hardware acceleration. The FPGA-deployed U-Net achieved 98.16% with a false positive rate of 0.9%, providing robust segmentation even in challenging conditions, making it the preferred model for reliable inference onboard PSG. The CAE model maintained 97.02% accuracy while achieving over 2× faster inference (31.81 ms vs. 74.29 ms per image). Both models enable accurate cloud segmentation with real-time inference, meeting the operational constraints derived from the secondary camera design, while operating at an average power consumption of 2.6 W (U-Net) and 2.4 W (CAE), well within the mission power constraint for the HW accelerator. Our results validate the feasibility of integrating DL-based cloud coverage assessment onboard PSG, contributing to the broader effort of advancing artificial intelligence-powered computing for EO missions to enable more autonomous data processing and decision-making.

The Italian Space Agency (ASI) under the "PLATiNO" multi-satellite constellation program have started the PLATiNO-3 and PLATiNO-4 payloads addressing Very High Resolution and Hyperspectral capabilities with small payloads under 100 kg, respectively. Proto-flight models of these instruments will be completed by 2024-2025. Under the same program, PLATiNO-1, a SAR mission in X-band and PLATiNO-2, a collaboration with NASA-JPL for the MAIA payload, will complete the constellation.In 2019 ASI launched the PRISMA instrument and from this baseline is developing a new "best-in-class" compact Hyperspectral payload (HYP-PL) that offers the same performance of its larger predecessor in a size and mass that can be adapted to multiple platforms in the small size range.In this article we present the main characteristics of the PLATiNO-4 payload.The HYP-PL is a <100Kg, single imaging spectrometer devoted to measure the spectral signature across the wavelength range 400-2500 nm by means of a diffraction grating as dispersing element. This spectral range matches many end-user requirements for environmental and commercial applications, such as water quality monitoring, oil spills, forest fire, soil protection, agriculture.The payload, after the successful development of breadboards and development models, is entering the D phase with the MAIT of the PFM.

In this study a novel architecture of Pulse Coupled Neural Network based on a multilevel topology with interconnected layers is presented. The model is applied to the solution of a segmentation problem of SAR images for the identification of man-made structures on urban landscapes. Thanks to the multilayer architecture, the unsupervised model can deal with dual polarization SAR data, as well as with combination of ascending and descending acquisitions. Such approach mitigates the issues in detecting artificial targets when their orientation with respect the satellite line of sight reduces the object backscattering with respect to the one of background. An example of application to two COSMO-SkyMed STRIPMAP data, acquired by ascending and descending orbits respectively is provided.The proposed approach, owing to their ability of efficiently processing voluminous datasets, can be effectively coupled with machine learning and deep learning to refine or validate their results.

In this paper the role of the COSMO-SkyMed constellation data in the framework of the Italian Space Agency (ASI)’s "Multimission and Multifrequency SAR" program has been analyzed. The key aspects regarding the provenance of the Principal Investigators, the application fields and information about the exploitation of the COSMO-SkyMed data, for both the first- and second-generation satellites, are highlighted in the study.

In this work the classification of Sentinel-3 Sea and Land Surface Temperature (S3-SLSTR) images with a focus on volcanic cloud detection through a Neural Networks (NNs) approach is presented. Since the hazardous nature of eruptions, a fast and reliable method to monitor the evolution of volcanic clouds in real time is of primary interest. NNs represent a suitable tool for this purpose given their short processing time once trained, and their ability to solve complex problems as those related to natural events. The present research starts from the generation of the training patterns by means of MODerate resolution Imaging Spectroradiometer (MODIS) data collected during the 2010 Eyjafjallajokull (Iceland) eruption, it goes through the training of the NN, and ends with the application of the NN-based model to SLSTR data collected during the 2019 Raikoke (Kuril Island, Russia) eruption.

In this study we present a novel approach dedicated to the retrieval of volcanic ash parameters by means of data acquired from the Sea and Land Surface Temperature Radiometer on board of the Copernicus Sentinel-3. In this framework, we developed a procedure combining Radiative Transfer Model simulations and Neural Network for estimating three volcanic ash parameters such as aerosol optical depth, effective radius and ash mass. The Radiative Transfer Model simulations have been considered for producing synthetic training sets, which have been used in the training phase of the Neural Networks development. In particular, nine latitude's belts have been identified for training several Neural Networks ensuring the global coverage of the method. The approach has been tested by comparing the results of the trained NN with the ones obtained by applied the state-of-art Look Up Table and the Volcanic Plume Retrieval procedures. The results of the methodologies applied on Raikoke, 2019 eruption demonstrated the feasibility of the proposed approach by registering values of the correlation coefficient between all the three methods ranging between the 65% and the 94%.

In this work a novel High Performance Computing Platform aimed at streamline the application of demanding Earth Observation algorithms is presented. The platform is today in the operational phase and it is opened to new users. The development of the High Performance Computing (HPC) services has been carried out during the execution of the DYDAS project, that was aimed at developing a collaborative platform for offering data, algorithms, processing and analysis services to a large number of users from different public and private user communities. The platform will also act as an e-marketplace enabling transactions for accessing data and added value services enabled by HPC and based on Big Data technologies, machine learning, AI and advanced data analytics. Such strategy has been implemented with the purpose to match demand and offer among those who own intellectual properties on data/methods for their use and those who need or want to exploit them. In this manuscript, an example of data processing using Copernicus Sentinel-1 and Sentinel-2 data in a supervised classification exercise is presented.

In this study, the potentiality of an automatic processing system for fast implementation of interferometric SAR analysis is tested over a time series of COSMO-SkyMed data. A system architecture for the end-to-end implementation of Permanent Scatterer workflow is illustrated and applied to a test case located over the South-West area of the Rome area. The system architecture is aimed at providing a high degree of automation in the different steps of the procedures, from the Satellite products download and storage up to the data visualization. The proposed solution can be integrated with both commercial processing tools and open source frameworks. A two years of COSMO-SkyMed acquisitions from the MAPITALY archive have been analysed with SARscape and a customized open source workflow. The obtained results highlighted some interesting features on the considered area of interest. Moreover, the outcome of the analyses are in good agreement with previous studies on the same geographical region.

In recent years many studies concerning the monitoring of volcanic activity have been carried out to develop ever more accurate and refine methods which allow to face the emergencies related to an eruption event. In our work we present different approaches for the volcanic ash cloud detection and retrieval using Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) data. As test case the SLSTR image collected on Raikoke volcano the 22 June 2019 at 00:07 UTC has been considered. A neural network based algorithm able to detect and distinguish volcanic and meteorological clouds, and the underlying surfaces, has been implemented and compared with two consolidated approaches: the RGB (Red-Green-Blue) and the Brightness Temperature Difference procedures. For the ash retrieval parameters (aerosol optical depth, effective radius and ash mass), three different methods have been compared: the reliable and consolidated LUTp(Look Up Table) procedure, the very fast VPR (Volcanic Plume Retrieval) algorithm and a neural network based model.

During a volcanic eruption, large quantities of Sulphur dioxide (SO2) are sometimes emitted into the atmosphere. Rapid detection and tracking ofvolcanic SO2 clouds might be beneficial to air traffic security and to predict any correlated impact on the environment; for example, the possibility of acid rain events. Within the presented work, we exploited Sentinel-5p radiance data (Level 1 b) to detect and retrieve SO2 volcanic emissions through a neural network based algorithmthat produces rapid SO2 vertical column estimates. The dataset used for training the net was composed of 13 TROPOMI Level 2 “Offline” SO2 data collected during the Etna Volcano eruption that occurred in 2018 from 22 December to 1 January. Experimental results are very encouraging and open to the perspective ofmake available a new and stable product for monitoring atmospheric SO2 clouds on a global scale based on Sentinel-5p acquisitions.

In this paper a new approach based on the use of Synthetic Aperture Radar COSMO-SkyMed products to verify urban change detection and to observe new constructions is presented. SLC products information has been exploited, since the proposed procedure combines backscattering coefficient and coherence information as extracted from two interferometric data, acquired in a short time interval. The algorithm exploits the information from backscatter intensity and interferometric coherence. Firstly, the interferometric SAR couple is processed by an unsupervised Neural-Networks, particularly PCNN (Pulse Coupled Neural Network) is applied to create a preliminary changes map based on the difference of backscatter intensity information. Then, accuracy is further improved by the fusion with the coherence information. The achieved results shown as the combination of backscattering and coherence information, extracted from Very High Resolution SAR data, allows to provide very accurate urban change detections with a fast and unsupervised procedure. The latter is particularly suitable to process quickly huge amount of SAR data since its lower computational requirements with respect to e.g. supervised algorithms.

In this work data observed by the geostationary MSG-SEVIRI and the polar NASA-Terra/Aqua-MODIS orbiting satellite instruments, have been used for the proximal and distal monitoring of the 24-30 December 2018 Etna eruption. The combined use of the SEVIRI high repetition time and the MODIS high spatial resolution allows a reliable near real time volcanic characterization from the source to the atmosphere. For the proximal monitoring the parameters estimated are the eruption starts and duration and the volcanic plume top height, while the distal monitoring was inverted relying on the determination of the volcanic cloud altitude and the ash/SO2retrievals. Achieved products were validated by comparing these results with those observed remotely by ground based networks.Results obtained in this study show the ability of satellite-based systems to entirely follow eruptive events in near real time, offering a powerful tool to mitigate volcanic risk on both local population and airspace.

In this paper a new approach based on the fusion of Sentinel-1 and Sentinel-2 products to map urban change detection and to observe suburb's development is presented. The algorithm developed can process data in a fast, automatic and accurate way. To reach this goal, the processing chain uses an iterative multitemporal approach based, for each iteration, on three procedures. The first and second ones are based on Pulse Coupled Neural Network (PCNN) applied to SAR and optical images, respectively, while the third processing is an optical multiband filter, implementing the spectral difference computation. The three outputs of each iteration are fused together by means of a weighted average formulation. The algorithm may deal with multitemporal acquisitions to improve the overall accuracy in the detection of urban changes by the integration of the outputs at different time intervals.

A Neural Network approach to classify Sentinel-3 sea and land surface temperature radiometer (SLSTR) pixels over polar regions is presented. The proposed approach is based on a careful preliminary analysis aimed to simulate SLSTR observation by means of MODIS data. The latter have been considered because of the long available time series and the quality of cloud mask products. A large set of MODIS AQUA and TERRA products has been applied to develop the training set of the Neural Network classificator that has been tuned to discriminate clear ocean, clouds and sea-ice surfaces on the scene.

In this manuscript, we address the problem of change detection for Sentinel-2 data. The proposed method is based on deep features representation. First, multilevel convolutional neural network (CNN) features are extracted from input images acquired at different times. Then, euclidean distance is applied to generate dissimilarity map that indicate change probabilities of each pixel. Finally, bounding boxes corresponding the change areas can be obtained with clustering and an optimizing connected component labeling algorithm. Experiments on a manually annotated dataset demonstrate the feasibility and effectiveness of the proposed method.

Volcanic eruptions affect at different levels the population and economy of interested areas. Moreover, volcanic ash detection represents a key issue for aviation safety due to the harming effects on aircraft. For these reasons, an accurate and fast analysis of the data is needed to monitor the phenomena's evolution and to manage the risk mitigation phase. In this scenario, the introduction of an inversion approach based on Neural Networks (NNs) has significant interest to reduce the need of human interpretation of the ash detection maps as those generated by the application of brightness temperature difference approach. In this work we show that NNs algorithms are suitable for an accurate mapping of ash cloud on Moderate Resolution Imaging Spectroradiometer (MODIS) images in a very cloudy scenario as the ones of 2010 Eyjafjallajökull and 2011 Grimsvötn eruptions.

Concurrent availability of VHR (Very High Resolution) images at both optical and microwave bands opens new challenges in many applicative scenarios of Earth Observation (EO). In particular this is true for precision farming activities where the retrieval on the metric scale of biophysical parameters and of information regarding vegetation spatial distributions can be very effective in supporting farmers during the production cycles. However, the inversion problem giving the value of the desired variable from the measured electromagnetic quantities (the image data) can be very complex and the nonlinear relationships involved need to be handled by suitable algorithms. In this paper a complete processing scheme providing quantities of interest for precision viticulture from data provided by WorldView-2 (WV2) and COSMOSkyMed (CSK) space platforms is presented. Once the appropriate season time was selected, the satellite data have been acquired over the test area within a limited time window and concurrently with the collection of the groundtruth. The workflow, besides adequate pre-processing steps, includes two neural networks (NN) modules, one is dedicated to the extraction of a restricted number of nonlinear components from the WV2 data, the other one to the actual inversion problem. The obtained results seem to be satisfactory with respect to the requirements provided by the users.

LeanEO! is a 2-year Earth Observation education project funded by the European Space Agency (ESA) and developed by different European Institutions. Its main aim is to increase the understanding and knowledge of satellite data obtained from ESA missions and demonstrate how these can be used when faced with environmental problems in the real world. The project has developed hands-on training resources for use primarily (but not exclusively) by teachers and students at upper high school to university level. Each lesson comes complete with data, analysis tools and exhaustive background information necessary for the completion of the suggested activities and provides answers to the various study questions. Model answers are supplied for users working on their own or with limited specialist support. In this paper the aims and the opportunities provided by the project will be described in detail.

We have analysed the seismic source of the active fault generated Van Mw=7.1 earthquake occurred in Eastern Turkey the 23rdOctober 2011. To this aim the surface displacement field has been measured applying SAR Interferometry (InSAR) technique to the available dataset of coseismic COSMO-SkyMed image pairs. The seismic source model has been obtained by the use of a data inversion procedure based on the concurrent application of InSAR techniques and Neural Networks. The proposed approach elaborates the information on the coseismic deformation pattern stemming from available differential interferograms. The interferogram is the expression of the active fault at depth, thus its shape, size and its features somehow refer to the geometry and slip of the fault generating the seism. A Neural Network has been trained to recognize some fault parameters (Length, Width, Strike, Dip, Depth) from the unwrapped interferogram. The retrieval exercise consists in estimating these parameters from the coseismic interferogram exploiting Neural Networks.

The possibility offered by space-based sensors represents an irreplaceable resource for monitoring in near real time the eruption activities. The high revisit time of sensor like MODIS, seems to be the most effective way to mitigate the aviation hazard imaging the phenomenon evolution. In this work we propose a neural networks based approach to the volcanic ash mass retrieval. In comparison with the techniques based on radiative transfer models, the proposed algorithm has shown similar accuracy and faster computation. This issue can be of real interest to address the problems inherent the volcanic activity in short time. A set of MODIS images collected during the Eyjafjallajokull eruption, occurred from the 14thof April to the 23rdof May 2010, has been used to analyze the performance variations due to different selection of the algorithm inputs, i.e. the MODIS channels from visible to thermal infrared electromagnetic spectrum. The best wavelength sets for the retrieval of the ash mass, optical thickness and effective radius have been identified by means of neural network pruning algorithm.

The great eruption of the Icelandic Eyjafjallajokull volcano that occurred from the 14thof April to the 23rdof May 2010 injected large and dense ash clouds into the atmosphere, causing major international air traffic disruption worldwide.

The basic idea of this paper relies on the concurrent exploitation of the capabilities of neural networks (NNs) and SAR interferometry (InSAR) for the characterization of a seismic source and the estimation of its geometric parameters. When a moderate-to-strong earthquake occurs, we can apply the InSAR technique to compute a differential interferogram. The earthquake is generated by an active seismogenic fault having its own specific geometry. The corresponding differential interferogram contains, in principle, information concerning the geometry of the seismic source that the earthquake comes from. To perform the inversion operation, a novel approach based on NNs is considered. This requires the generation of a statistically significant number of synthetic interferograms necessary for the network training phase. Each of them corresponds to a different combination of fault geometric parameters. After the training, the network is ready to perform, in real time, the inversion on new differential interferograms. This paper illustrates such a methodology and its validation on a set of experimental data.

The basic idea of this paper relies on the concurrent exploitation of the capabilities of neural networks and SAR interferometry for the characterization of a seismic source and the estimation of its geometric parameters. When a moderate-to-strong earthquake occurs we can apply SAR Interferometry (InSAR) technique to compute a differential interferogram. The earthquake has been generated by an active, seismogenic, fault having its own specific geometry. Therefore each differential interferogram contains in principle information concerning the geometry of the seismic source the earthquake comes from. To perform the inversion operation an approach based on neural networks can be considered. The paper illustrates such a methodology and its assessment on experimental data.