{"id":8,"date":"2024-09-04T18:51:09","date_gmt":"2024-09-04T16:51:09","guid":{"rendered":"https:\/\/spaceraise.academy\/pasteditions\/2025\/?page_id=8"},"modified":"2025-10-10T10:18:47","modified_gmt":"2025-10-10T08:18:47","slug":"homepage","status":"publish","type":"page","link":"https:\/\/spaceraise.academy\/pasteditions\/2025\/","title":{"rendered":"Homepage"},"content":{"rendered":"\n\n\t\n\t\t\t\t<p class=\"rs-p-wp-fix\"><\/p>\n\t\t\t\t<sr7-module data-alias=\"hero-1-1\" data-id=\"3\" id=\"SR7_3_1\" class=\"rs-ov-hidden\" data-version=\"6.7.28\">\n\t\t\t\t\t<sr7-adjuster><\/sr7-adjuster>\n\t\t\t\t\t<sr7-content>\n\t\t\t\t\t\t<sr7-slide id=\"SR7_3_1-5\" data-key=\"5\">\n\t\t\t\t\t\t\t<sr7-bg id=\"SR7_3_1-5-9\" class=\"sr7-layer\"><noscript><img decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/10\/real_europe_west_dark.png\" alt=\"\" title=\"real_europe_west_dark\"><\/noscript><\/sr7-bg>\n\t\t\t\t\t\t\t<sr7-txt id=\"SR7_3_1-5-0\" class=\"sr7-layer\">L'Aquila, Italy 12-30 May 2025<\/sr7-txt>\n\t\t\t\t\t\t\t<a id=\"SR7_3_1-5-2\" class=\"sr7-layer\" href=\"\/#registration\" target=\"_self\">Registration is closed <i class=\"fa-chevron-right\"><\/i><\/a>\n\t\t\t\t\t\t\t<sr7-txt id=\"SR7_3_1-5-4\" class=\"sr7-layer\">SPACERAISE,<\/sr7-txt>\n\t\t\t\t\t\t\t<sr7-txt id=\"SR7_3_1-5-5\" class=\"sr7-layer\">for the Space Sector<\/sr7-txt>\n\t\t\t\t\t\t\t<sr7-txt id=\"SR7_3_1-5-8\" class=\"sr7-layer\">the \"International Doctoral School\"<\/sr7-txt>\n\t\t\t\t\t\t<\/sr7-slide>\n\t\t\t\t\t\t<sr7-slide id=\"SR7_3_1-6\" data-key=\"6\">\n\t\t\t\t\t\t<\/sr7-slide>\n\t\t\t\t\t<\/sr7-content>\n\t\t\t\t\t<image_lists style=\"display:none\">\n\t\t\t\t\t\t<img data-src=\"\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/10\/hires-satellite-mid-transparent2.png\" data-libid=\"132\" data-lib=\"medialibrary\" title=\"hires-satellite-mid-transparent2\" width=\"0\" height=\"0\" data-dbsrc=\"Ly9zcGFjZXJhaXNlLmFjYWRlbXkvcGFzdGVkaXRpb25zLzIwMjUvd3AtY29udGVudC91cGxvYWRzLzIwMjQvMTAvaGlyZXMtc2F0ZWxsaXRlLW1pZC10cmFuc3BhcmVudDIucG5n\"\/>\n\t\t\t\t\t\t<img data-src=\"\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/10\/real_europe_west_dark.png\" data-libid=\"137\" data-lib=\"medialibrary\" title=\"real_europe_west_dark\" width=\"0\" height=\"0\" data-dbsrc=\"Ly9zcGFjZXJhaXNlLmFjYWRlbXkvcGFzdGVkaXRpb25zLzIwMjUvd3AtY29udGVudC91cGxvYWRzLzIwMjQvMTAvcmVhbF9ldXJvcGVfd2VzdF9kYXJrLnBuZw==\"\/>\n\t\t\t\t\t<\/image_lists>\n\t\t\t\t<\/sr7-module>\n\t\t\t\t<script>\n\t\t\t\t\tSR7.PMH ??={}; SR7.PMH[\"SR7_3_1\"] = {cn:100,state:false,fn: function() { if (_tpt!==undefined && _tpt.prepareModuleHeight !== undefined) {  _tpt.prepareModuleHeight({id:\"SR7_3_1\",el:[700,700,700,700,720],type:'hero',shdw:'0',gh:[700,700,700,700,720],gw:[1060,1060,1060,1060,480],vpt:['-100px&#039;,&#039;-100px&#039;,&#039;-100px&#039;,&#039;-100px&#039;,&#039;-100px'],size:{fullWidth:false, fullHeight:false},mh:'0',onh:0,onw:0,bg:{color:'{\"orig\":\"transparent\",\"type\":\"solid\",\"string\":\"transparent\"}'}});   SR7.PMH[\"SR7_3_1\"].state=true;} else if(SR7.PMH[\"SR7_3_1\"].cn-->0)\tsetTimeout( SR7.PMH[\"SR7_3_1\"].fn,19);}};SR7.PMH[\"SR7_3_1\" ].fn();\n\t\t\t\t<\/script>\n\n\t\t\t\t<a href=\"https:\/\/www.gransassotech.org\/\"  target=\"_blank\" rel=\"noopener\"  itemprop=\"url\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/10\/gransasso_tech_logo_1.svg\" alt=\"Gran Sasso Tech\" itemprop=\"image\" height=\"150\" width=\"850\" title=\"Gran Sasso Tech\" onerror=\"this.style.display='none'\"  \/>\n\t\t\t\t<\/a>\n\t\t\t\t<a href=\"https:\/\/www.gssi.it\/\"  target=\"_blank\" rel=\"noopener\"  itemprop=\"url\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/10\/Gran-Sasso-Science-Institute.png\" alt=\"Gran Sasso Science Institute\" itemprop=\"image\" height=\"118\" width=\"300\" title=\"Gran Sasso Science Institute\" onerror=\"this.style.display='none'\"  \/>\n\t\t\t\t<\/a>\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/10\/barra-loghi-rome-technopole-Trasparente.png\" alt=\"barra loghi rome technopole Trasparente\" itemprop=\"image\" height=\"360\" width=\"2686\" title=\"barra loghi rome technopole Trasparente\" onerror=\"this.style.display='none'\"  \/>\n<a  data-type=\"box\" data-name=\"Box\" data-accepts=\"true\" href=\"#week1\" target=\"_self\">\n\t<\/a>\n<h4>\n\t\tWeek #1\n\t<\/h4>\n<h4>\n\t\t12 \u2192 16 May 2025\n\t<\/h4>\n<h3>\n\t\tRobotics for Space Applications\n\t<\/h3>\n<a  data-type=\"box\" data-name=\"Box\" data-accepts=\"true\" href=\"#week2\" target=\"_self\">\n\t<\/a>\n<h4>\n\t\tWeek #2<br \/>\n\t<\/h4>\n<h4>\n\t\t19 \u2192 23 May 2025\n\t<\/h4>\n<h3>\n\t\tDigital Twins for Space Manufacturing and Satellites\n\t<\/h3>\n<a  data-type=\"box\" data-name=\"Box\" data-accepts=\"true\" href=\"#week3\" target=\"_self\">\n\t<\/a>\n<h4>\n\t\tWeek #3\n\t<\/h4>\n<h4>\n\t\t26 \u2192 30 May 2025\n\t<\/h4>\n<h3>\n\t\tArtificial Intelligence for Aerospace\n\t<\/h3>\n<h2>\n\t\tPROJECT<br \/>\n\t<\/h2>\n\tSPACERAISE consists of an intensive training program with a high technological and scientific content aimed at young talents who want to understand the major challenges in the field of space software engineering and acquire new skills in the areas of <i><b>Artificial Intelligence<\/b><\/i>, <i><b>Robotics<\/b><\/i>, and <i><b>Digital Twins<\/b><\/i> for aerospace applications. The program aims to deliver advanced and specialised training to meet the emerging needs of the aerospace and technology sectors. It will use a multidisciplinary approach that integrates basic research from the Computer Science department at GSSI, insights from prestigious international faculty, and real-world industrial applications. The program includes the implementation of three intensive doctoral schools, structured into three distinct specialisation modules, covering the most relevant challenges of software engineering in the space domain.\n\t<iframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/GXBqecwFCoY?si=JW800887PTsMN33i\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n\t<iframe loading=\"lazy\" title=\"Final Chapter SPACERAISE &quot;International Doctoral School&quot; for the Space Sector, Italy 12-30 May 2025\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/YVrVQ3dCoUE?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<h2>\n\t\tCOURSES\n\t<\/h2>\n\t<p>Each specialization course is designed to last no longer than one week. Additionally, by limiting each topic to a maximum of 60 students, the program ensures a focused and personalized approach that fosters meaningful interactions between participants and instructors. This allows for greater individual attention and deeper learning opportunities. The Doctoral School is open to the following profiles:<\/p>\n<ul>\n<li>\nMaster&#8217;s students\n<\/li>\n<li>\nPhD students\n<\/li>\n<li>\nResearchers\n<\/li>\n<li>\nIndustry professionals\n<\/li>\n<\/ul>\nThis structure aims to create a dynamic and interdisciplinary learning environment, encouraging the exchange of ideas and experiences among participants from diverse backgrounds and career stages.\n<p><strong>Participants can also enroll in individual courses. Enrollment in even just one of the courses grants the certificate of participation in the SPACERAISE doctoral school.<\/strong><\/p>\n<h3>\n\t\tArtificial Intelligence for Aerospace\n\t<\/h3>\n\tWeek #3 \u00a0 | \u00a0 <strong>Monday, May 26 \u2192 Friday, May 30<\/strong> \u00a0 | \u00a0 Auditorium &#8211; Palazzo del Rettorato, GSSI.\nThis module will discuss the use of Artificial Intelligence onboard satellites, addressing issues related to safety, and software certification in compliance with safety standards. Examples of AI applications that will be covered include predictive maintenance, image recognition, anomaly detection, remote sensing, and more.\n\t\t\t<a href=\"\" target=\"_self\">\n\t\t\t\t\t\t\tShow schedule details\n\t\t\t<\/a>\n<hr \/><h5>The course schedules are in the process of being finalized and are subject to minor changes.<\/h5><hr \/><h3>May 26, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><h5><i>Registration + Keynote: AI and the infinite: the intersection of AI and Space ventures<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/elena-grifoni-winters\/\">Elena Grifoni Winters<\/a> <mark>GSSI<\/mark><\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Trustworthiness and explainability in AI for Space application<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/arturo-intelisano\/\">Arturo Intelisano<\/a> <mark>Thales Alenia Space Italia<\/mark><p>As artificial intelligence (AI) increasingly plays a pivotal role in space applications-ranging from satellite operations to autonomous navigation and data analysis-it is crucial to establish a trustworthy framework for its deployment. Trustworthiness in AI is essential to ensure reliability, safety, and integrity in space missions. To achieve this, identifying and defining specific metrics based on explainable mechanisms and theories is fundamental. This approach facilitates transparency in AI decision-making, particularly when using complex models like neural networks. By focusing on interpretability and accountability, researchers can enhance the predictability of AI systems, allowing stakeholders to understand how decisions are made and ensuring that AI solutions align with mission objectives while mitigating risks.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><h5><i>AI\/ML for safety-critical software: the case of the space domain <\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-basciani\/\">Francesco Basciani<\/a> <mark>GSSI<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/alberto-petrucci\/\">Alberto Petrucci<\/a> <mark>Thales Alenia Space Italia<\/mark><\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>New generation of virtualization infrastructure in space<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/andrea-danieli\/\">Andrea Danieli<\/a> <mark>Telespazio<\/mark>The course explores the evolution of virtualization infrastructures applied to space systems, focusing on the adoption of containerization technologies and orchestration tools to manage highly dynamic environments and limited resources.<br \/>\nParticipants will discover how the use of containers (e.g. Docker) allows to isolate critical applications, improving security and operational flexibility in space mission conditions.<br \/>\nStrategies for orchestrating distributed containers (with tools such as Kubernetes) will be analyzed, in order to ensure effective resource management, automation of deployment processes and system resilience in the presence of peculiar challenges such as environmental alterations and energy limitations.<br \/>\nThe training path offers an integrated overview of the design and implementation of intelligent virtual infrastructures for space environments.<br \/>\nThe course also delves into aspects such as workload sizing, performance monitoring and integration with hybrid cloud systems, providing participants with the skills needed to innovate onboard process management and resilience-oriented network configuration.<br \/>\nIdeal for engineers, researchers and professionals in the aerospace and IT sectors, this course prepares specialists to face the challenges of digitalization and to design solutions capable of supporting next-generation space missions, ensuring safety, scalability and operational efficiency.\n<\/td><\/tr><\/tbody><\/table><h3>May 27, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Introduction to AI in Space Safety and Sustainability<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/federica-paganelli\/\">Federica Paganelli<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-evangelisti\/\">Francesco Evangelisti<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/tobia-giani\/\">Tobia Giani<\/a> <mark>Aiko Space<\/mark><p>This session explores how Artificial Intelligence is shaping the future of space safety and sustainability. Topics include the use of AI for collision avoidance, debris management, and autonomous decision-making in satellite operations. Practical use cases will demonstrate how AI enhances operational efficiency, reduces human intervention, and contributes to sustainable space activities.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Applying AI to Mission Design: Working Groups<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/federica-paganelli\/\">Federica Paganelli<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-evangelisti\/\">Francesco Evangelisti<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/tobia-giani\/\">Tobia Giani<\/a> <mark>Aiko Space<\/mark><p>Participants will collaborate in groups to design mission scenarios that leverage AI for sustainability-focused objectives. These include designing autonomous collision-avoidance systems, optimizing satellite constellations, and planning debris mitigation strategies. Facilitators will guide discussions and provide examples of AI solutions in real-world applications.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;13:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>13:00&nbsp;&#8211;&nbsp;14:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Introduction to Machine Learning for Onboard Data Analysis in Earth Observation<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-rossi\/\">Francesco Rossi<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/armando-la-rocca\/\">Armando La Rocca<\/a> <mark>Aiko Space<\/mark><p>This session provides an overview of machine learning (ML) techniques for real-time data processing onboard satellites. Key topics include the use of ML for Earth observation data, such as anomaly detection, environmental monitoring, and rapid disaster assessment. Participants will gain insights into how onboard AI models can operate under constraints like limited computational power and latency.<\/p>\n<\/td><\/tr><tr><td>14:30&nbsp;&#8211;&nbsp;15:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>15:00&nbsp;&#8211;&nbsp;16:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Hands-On: Implementing a Neural Network-Based Classification Algorithm<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-rossi\/\">Francesco Rossi<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/armando-la-rocca\/\">Armando La Rocca<\/a> <mark>Aiko Space<\/mark><p>In this hands-on session, participants will implement a neural network to classify Earth observation data. The session covers essential steps, from dataset preprocessing to model training and performance evaluation. Examples include classifying land use, detecting changes in vegetation, and identifying urban growth patterns. The workshop will emphasize practical implementation and provide participants with tools to adapt the algorithm for various use cases.<\/p>\n<\/td><\/tr><tr><td>16:30&nbsp;&#8211;&nbsp;18:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>A trillion dollars economy? Space economy fundamentals trends and externalities<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/elena-grifoni-winters\/\">Elena Grifoni Winters<\/a> <mark>GSSI<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/pierre-lionnet\/\">Pierre Lionnet<\/a> <mark>Eurospace<\/mark><p>The space economy has now become a subject of discussion in mainstream media and political circles, especially since the publication by financial institutions and major consultancies of prospects for its exponential growth (the infamous Trillion Dollar Space Economy projections) and the increased focus given to the &#8216;newspace&#8217; and private equity investments in the space sector. In this lecture we will look at an economist perspective on space markets and industrial structures, focusing on infrastructure development and financing, and delivering a data-based analysis of major trends affecting the space sector in the past decades, inlcuding actual, or perceived, paradigm changes. The lecture will review the variety of good (and not so good) data sources and methodologies to accurately measure the economic outturn of space activities, stressing the notions of markets (for infrastructure and services) and externalities (positive and negative) and will deliver an organised toolbox to assess the economics of space activities worldwide.<\/p>\n<\/td><\/tr><\/tbody><\/table><h3>May 28, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Automated testing of deep neural networks for computer vision tasks in space<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/fabrizio-pastore\/\">Fabrizio Pastore<\/a> <mark>University of Luxemburg<\/mark><p>In safety- and mission-critical space systems such as rovers and satellites, Deep Neural Networks (DNNs) are becoming a key component for computer vision tasks, particularly object detection and autonomous navigation. However, since the DNN behavior cannot be assessed through code inspection and analysis, test automation has become an essential activity to gain confidence in the reliability of DNNs. <\/p>\n<p>Since DNNs for computer vision operate on large input spaces (e.g., any possible landscape for a Mars rover) traditional DNN testing approaches leveraging data sets with a limited number of pictures collected in the field might not enable the detection of all DNN limitations. For this reason, simulators, which may generate potentially infinite number of diverse images along with additional information to be used as ground-truth, have become a valid tool for DNN testing. But generating random images might not be the most effective solution.<\/p>\n<p>This talk provides an overview of recent research approaches combining simulators with evolutionary search algorithms, which, driven by appropriate fitness functions, can determine what are the worst-case scenarios for the DNNs under test. In addition, we will show how the lack of fidelity can be effectively addressed by leveraging Generative Adversarial Networks (GANs), to transform the data generated by simulators into realistic input images. Such images can be used both to assess the DNN performance and to retrain the DNN more effectively. The talk will finally provide insights on the results achieved in the context of the ESA projects TIA.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Supporting safety analysis of Deep Neural Networks through failure explanation<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/fabrizio-pastore\/\">Fabrizio Pastore<\/a> <mark>University of Luxemburg<\/mark><p>Deep neural networks are increasingly important in safety-and mission-critical systems, including space systems, for example, in their perception layer to analyze images. Unfortunately, there is a lack of methods to ensure the functional safety of DNN-based components, specifically we identify three major challenges with existing practices: 1) situations that are under-represented in the test set may lead to serious safety violation risks but may, however, remain unnoticed; 2) characterizing such high-risk situations is critical for safety analysis; 3) retraining DNNs to address these risks is poorly supported when causes of violations are difficult to determine. <\/p>\n<p>This lessons provides an overview of failure explanation approaches for DNNs that enable the detection of the distinct situations in which a DNN may fail and, consequently, enable the identification of countermeasures. Particular emphasis will be given to analysis pipelines that enable the identification of such situations by identifying cluster of similar failure-inducing images. They combine transfer learning, autoencoders, heatmaps of neuron relevance, dimensionality reduction techniques, and different clustering algorithms. The lesson will provide the results obtained in the context of the ESA project TIA and will include hands-on sessions to experiment with the presented solutions.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Analysis of Perception Neural Networks via Vision-Language Models<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/corina-pasarenau\/\">Corina Pasarenau<\/a> <mark>Carnegie Mellon University, NASA<\/mark><p>The analysis of Deep Neural Networks (DNNs), particularly those used as perception modules, is very challenging due to the networks&#8217; complex and opaque decision-making processes. Multi-modal Vision-Language Models (VLMs) such as CLIP offer an exciting opportunity to interpret the representation space of vision models using natural language. VLMs have been trained on a large body of images accompanied by their textual description, and are thus implicitly aware of high-level, human-understandable concepts describing the images. In this talk, we report on on-going work that seeks to leverage VLMs for the formal analysis and run-time monitoring of requirements expressed in terms of natural-language concepts, as well as debugging of perception modules.<\/p>\n<\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><h5><i>Social event<\/i><\/h5><\/td><\/tr><\/tbody><\/table><h3>May 29, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>AI for Earth Observation (AI4EO) in action: Insights and Innovations<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/cristiano-nattero\/\">Cristiano Nattero<\/a> <mark>WASDI<\/mark><p>Explore the potential and challenges of Artificial Intelligence for Earth Observation (AI4EO). This session begins with insights from WASDI, showcasing real-world examples and case studies that highlight both the successes and limitations of AI4EO applications. From innovative use cases to persistent hurdles-such as ethical concerns, inflated expectations, algorithmic hallucinations, and reliability issues-we will examine the state of the art and identify key open problems. The lecture concludes with a detailed exploration of a specific case study, offering a balanced perspective on how AI can be leveraged for Earth Observation while addressing its current constraints.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Building a Cloud-Based Downstream Earth Observation Application with WASDI<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/cristiano-nattero\/\">Cristiano Nattero<\/a> <mark>WASDI<\/mark><p>This hands-on session will guide participants through the implementation of a basic downstream Earth Observation (EO) application in the cloud using WASDI. Starting from scratch, we&#8217;ll cover the process of algorithm design, data acces, and deployment in a cloud environment. Participants will learn how to leverage WASDI&#8217;s tools to simplify development, scale processing, and prototype EO services. By the end of this session, attendees will have the foundational skills to create and deploy their own EO applications, opening the door to scalable research and service-oriented projects.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>An Overview of Anomaly Detection for Time Series<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/paul-boniol\/\">Paul Boniol<\/a> <mark>University of Paris-Descartes<\/mark><p>Anomaly detection is an important problem in data analytics with applications in many domains. In recent years, there has been an increasing interest in anomaly detection tasks applied to time series. In this talk, we take a holistic view of anomaly detection in time series, starting from the core definitions and taxonomies related to time series and anomaly types, to an extensive description of the anomaly detection methods proposed by different communities in the literature. We will then present new benchmarks capturing diverse domains and applications for the purpose of evaluating anomaly detection methods. We will then conclude on Ensembling and Model Selection for time series anomaly detection, discussing different strategies applicable to automatically selecting the appropriate methods for a specific time series.<\/p>\n<\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>A deep dive into time series anomaly detection evaluation measures<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/paul-boniol\/\">Paul Boniol<\/a> <mark>University of Paris-Descartes<\/mark><p>Anomaly detection is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains where anomaly detection mainly focuses on point-based anomalies (i.e., outliers in standalone observations), anomaly detection for time series is also concerned with range-based anomalies (i.e., outliers spanning multiple observations). Nevertheless, it is common to use traditional point-based information retrieval measures, such as Precision, Recall, and Fscore, to assess the quality of methods by thresholding the anomaly score to mark each point as an anomaly or not. However, mapping discrete labels into continuous data introduces unavoidable shortcomings, complicating the evaluation of range-based contextual and collective anomalies. In this talk, we will dive into the existing evaluation measures to properly compare anomaly detection methods for time series. Finally, we will discuss how evaluation measures can be compared and evaluated.<\/p>\n<\/td><\/tr><\/tbody><\/table><h3>May 30, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Automating spaceborne Earth observation data exploitation through deep learning: current status and future perspectives<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/filippo-britti\/\">Filippo Britti<\/a> <mark>e-Geos<\/mark><p>Thanks to the recent progress in artificial intelligence and the continuous improvements on parallel computing capabilities, the application of computer vision methodologies within the satellite remote sensing field is becoming increasingly a reality for Earth observation (EO) purposes. The present talk will therefore aim at showing current status of principal deep-learning based solutions for traditional EO tasks such as object detection and classification, data preparation, land-cover classification and physical-biological parameters regression. Practical examples coming from both experimental and operational activities will be provided, leveraging on diverse satellite Earth observation technologies. Further, an outlook on future perspectives and impacts of such technologies will be discussed.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>ORBIT: Web application for research and planning of potential satellite acquisitions for detection<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/giusy-cappelluti\/\">Giusy Cappelluti<\/a> <mark>Fondazione CIMA<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/alessandro-cottino\/\">Alessandro Cottino<\/a> <mark>Fondazione CIMA<\/mark>Satellite data is very useful for evaluating the effects on the ground in the event of extreme events, such as floods, which increasingly affect our territory.<br \/>\nUsually, to search for possible useful acquisitions for emergency management and planning of interventions to help the populations of areas flooded or affected by an extreme event, it is necessary to access various portals and then summarize the best opportunities.<br \/>\nORBIT allows us to carry out a search for the main satellite acquisitions available (e.g. Sentinel, COSMO SkyMed) on a particular geographical area selected by the user in order to plan those of greatest interest for the case study.\n<p>Students will be invited to practice with the ORBIT tool starting from a case study.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Use of high-resolution remote sensing data for mapping the extent of water reservoirs and flooded areas: basic principles and applications<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/luca-pulvirenti\/\">Luca Pulvirenti<\/a> <mark>Fondazione CIMA<\/mark><p>Climate change is increasingly manifested by global increases in the frequency and magnitude of hydrological extremes (i.e., floods and droughts), with severe impacts on economies, livelihoods, and the environment. In a warming climate, both floods and droughts are likely to intensify (i.e., increasing in their duration, frequency, and severity). This can increase the likelihood of rapid shifts or overlap between these opposite hydrological extremes. For instance, in Northern Italy, a big flood affected Emilia-Romagna in May 2023 after the second winter drought in two years. The same region was again affected by floods in September and October 2024, while at the same time, southern Italy was affected by a severe drought. The latter, in turn, caused a significant shrinking of some lakes used for water supply of several municipalities.<\/p>\n<p>High resolution remote sensing data provide important information for flood mapping and lake extent monitoring. Concerning flood mapping, they allow emergency managers to rapidly have a synoptic overview of the extent of the inundation, as well as to monitor its temporal evolution. In this context, synthetic aperture radar (SAR) sensors represent the most commonly used instruments, because they can provide data both day and night and in almost any meteorological conditions.<\/p>\n<p>As for the monitoring of the extent of the water reservoirs, also optical data can be considered as a useful source of information. While they are often unsuitable for inundation mapping because of the frequent cloud cover associated with these disasters, they can be profitably exploited for a long-term lake extent monitoring (e.g., on a monthly basis), at regional or even national scale. This activity is helpful to identify possible situations of water scarcity.<\/p>\n<p>The presentation will illustrate the basic principles of remote sensing of water surfaces from optical and SAR data and will show some applications performed in collaboration with the Italian Department of Civil Protection. For SAR data, the presentation will show examples of the use of both Sentinel-1 and COSMO-SkyMed (1st and 2nd generation). Regarding optical data, the presentation will focus on Sentinel-2.<\/p>\n<\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Downscaling satellite precipitation fields using convolutional neural networks<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/edoardo-raparelli\/\">Edoardo Raparelli<\/a> <mark>University of L&#8217;Aquila<\/mark><p>Precipitation data derived from satellite observations is a crucial resource for hydrology, meteorology, and climate research, particularly in regions with limited ground-based monitoring. However, satellite precipitation estimates are often limited in spatial resolution, posing challenges for local-scale applications such as flood forecasting, water resource management, avalanche danger forecasting and agricultural planning. To address this, convolutional neural networks (CNNs) have emerged as a powerful tool for downscaling coarse-resolution images to high-resolution grids, enhancing its utility for fine-scale analyses. This lecture reviews the potential of CNN-based downscaling techniques for increasing spatial resolution of satellite derived precipitation data. We discuss the architecture of CNN models, their training processes on diverse datasets, and their ability to capture spatial patterns and intricate features within precipitation fields. Results demonstrate that CNN-based downscaling improves the spatial detail and accuracy of satellite derived precipitation data, underscoring the value of combining satellite data with advanced machine learning techniques to overcome observational limitations in Earth sciences.<\/p>\n<\/td><\/tr><\/tbody><\/table>\n<h3>\n\t\tDigital Twins for Space Manufacturing and Satellites\n\t<\/h3>\n\tWeek #2 \u00a0 | \u00a0 <strong>Monday, May 19 \u2192 Friday, May 23<\/strong> \u00a0 | \u00a0 Sala Ipogea and Giardino d&#8217;Inverno &#8211; Palazzo dell&#8217;Emiciclo, Regione Abruzzo.<br \/>\nThis module will explore the use of digital twins in the manufacturing of satellites within intelligent and automated factories, enabling various types of analysis, including predictive maintenance, predictive quality, and automated anomaly detection.\n\t\t\t<a href=\"\" target=\"_self\">\n\t\t\t\t\t\t\tShow schedule details\n\t\t\t<\/a>\n<hr \/><h5>The course schedules are in the process of being finalized and are subject to minor changes.<\/h5><hr \/><h3>May 26, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><h5><i>Registration + Keynote: AI and the infinite: the intersection of AI and Space ventures<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/elena-grifoni-winters\/\">Elena Grifoni Winters<\/a> <mark>GSSI<\/mark><\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Trustworthiness and explainability in AI for Space application<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/arturo-intelisano\/\">Arturo Intelisano<\/a> <mark>Thales Alenia Space Italia<\/mark><p>As artificial intelligence (AI) increasingly plays a pivotal role in space applications-ranging from satellite operations to autonomous navigation and data analysis-it is crucial to establish a trustworthy framework for its deployment. Trustworthiness in AI is essential to ensure reliability, safety, and integrity in space missions. To achieve this, identifying and defining specific metrics based on explainable mechanisms and theories is fundamental. This approach facilitates transparency in AI decision-making, particularly when using complex models like neural networks. By focusing on interpretability and accountability, researchers can enhance the predictability of AI systems, allowing stakeholders to understand how decisions are made and ensuring that AI solutions align with mission objectives while mitigating risks.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><h5><i>AI\/ML for safety-critical software: the case of the space domain <\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-basciani\/\">Francesco Basciani<\/a> <mark>GSSI<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/alberto-petrucci\/\">Alberto Petrucci<\/a> <mark>Thales Alenia Space Italia<\/mark><\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>New generation of virtualization infrastructure in space<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/andrea-danieli\/\">Andrea Danieli<\/a> <mark>Telespazio<\/mark>The course explores the evolution of virtualization infrastructures applied to space systems, focusing on the adoption of containerization technologies and orchestration tools to manage highly dynamic environments and limited resources.<br \/>\nParticipants will discover how the use of containers (e.g. Docker) allows to isolate critical applications, improving security and operational flexibility in space mission conditions.<br \/>\nStrategies for orchestrating distributed containers (with tools such as Kubernetes) will be analyzed, in order to ensure effective resource management, automation of deployment processes and system resilience in the presence of peculiar challenges such as environmental alterations and energy limitations.<br \/>\nThe training path offers an integrated overview of the design and implementation of intelligent virtual infrastructures for space environments.<br \/>\nThe course also delves into aspects such as workload sizing, performance monitoring and integration with hybrid cloud systems, providing participants with the skills needed to innovate onboard process management and resilience-oriented network configuration.<br \/>\nIdeal for engineers, researchers and professionals in the aerospace and IT sectors, this course prepares specialists to face the challenges of digitalization and to design solutions capable of supporting next-generation space missions, ensuring safety, scalability and operational efficiency.\n<\/td><\/tr><\/tbody><\/table><h3>May 27, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Introduction to AI in Space Safety and Sustainability<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/federica-paganelli\/\">Federica Paganelli<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-evangelisti\/\">Francesco Evangelisti<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/tobia-giani\/\">Tobia Giani<\/a> <mark>Aiko Space<\/mark><p>This session explores how Artificial Intelligence is shaping the future of space safety and sustainability. Topics include the use of AI for collision avoidance, debris management, and autonomous decision-making in satellite operations. Practical use cases will demonstrate how AI enhances operational efficiency, reduces human intervention, and contributes to sustainable space activities.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Applying AI to Mission Design: Working Groups<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/federica-paganelli\/\">Federica Paganelli<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-evangelisti\/\">Francesco Evangelisti<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/tobia-giani\/\">Tobia Giani<\/a> <mark>Aiko Space<\/mark><p>Participants will collaborate in groups to design mission scenarios that leverage AI for sustainability-focused objectives. These include designing autonomous collision-avoidance systems, optimizing satellite constellations, and planning debris mitigation strategies. Facilitators will guide discussions and provide examples of AI solutions in real-world applications.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;13:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>13:00&nbsp;&#8211;&nbsp;14:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Introduction to Machine Learning for Onboard Data Analysis in Earth Observation<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-rossi\/\">Francesco Rossi<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/armando-la-rocca\/\">Armando La Rocca<\/a> <mark>Aiko Space<\/mark><p>This session provides an overview of machine learning (ML) techniques for real-time data processing onboard satellites. Key topics include the use of ML for Earth observation data, such as anomaly detection, environmental monitoring, and rapid disaster assessment. Participants will gain insights into how onboard AI models can operate under constraints like limited computational power and latency.<\/p>\n<\/td><\/tr><tr><td>14:30&nbsp;&#8211;&nbsp;15:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>15:00&nbsp;&#8211;&nbsp;16:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Hands-On: Implementing a Neural Network-Based Classification Algorithm<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-rossi\/\">Francesco Rossi<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/armando-la-rocca\/\">Armando La Rocca<\/a> <mark>Aiko Space<\/mark><p>In this hands-on session, participants will implement a neural network to classify Earth observation data. The session covers essential steps, from dataset preprocessing to model training and performance evaluation. Examples include classifying land use, detecting changes in vegetation, and identifying urban growth patterns. The workshop will emphasize practical implementation and provide participants with tools to adapt the algorithm for various use cases.<\/p>\n<\/td><\/tr><tr><td>16:30&nbsp;&#8211;&nbsp;18:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>A trillion dollars economy? Space economy fundamentals trends and externalities<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/elena-grifoni-winters\/\">Elena Grifoni Winters<\/a> <mark>GSSI<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/pierre-lionnet\/\">Pierre Lionnet<\/a> <mark>Eurospace<\/mark><p>The space economy has now become a subject of discussion in mainstream media and political circles, especially since the publication by financial institutions and major consultancies of prospects for its exponential growth (the infamous Trillion Dollar Space Economy projections) and the increased focus given to the &#8216;newspace&#8217; and private equity investments in the space sector. In this lecture we will look at an economist perspective on space markets and industrial structures, focusing on infrastructure development and financing, and delivering a data-based analysis of major trends affecting the space sector in the past decades, inlcuding actual, or perceived, paradigm changes. The lecture will review the variety of good (and not so good) data sources and methodologies to accurately measure the economic outturn of space activities, stressing the notions of markets (for infrastructure and services) and externalities (positive and negative) and will deliver an organised toolbox to assess the economics of space activities worldwide.<\/p>\n<\/td><\/tr><\/tbody><\/table><h3>May 28, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Automated testing of deep neural networks for computer vision tasks in space<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/fabrizio-pastore\/\">Fabrizio Pastore<\/a> <mark>University of Luxemburg<\/mark><p>In safety- and mission-critical space systems such as rovers and satellites, Deep Neural Networks (DNNs) are becoming a key component for computer vision tasks, particularly object detection and autonomous navigation. However, since the DNN behavior cannot be assessed through code inspection and analysis, test automation has become an essential activity to gain confidence in the reliability of DNNs. <\/p>\n<p>Since DNNs for computer vision operate on large input spaces (e.g., any possible landscape for a Mars rover) traditional DNN testing approaches leveraging data sets with a limited number of pictures collected in the field might not enable the detection of all DNN limitations. For this reason, simulators, which may generate potentially infinite number of diverse images along with additional information to be used as ground-truth, have become a valid tool for DNN testing. But generating random images might not be the most effective solution.<\/p>\n<p>This talk provides an overview of recent research approaches combining simulators with evolutionary search algorithms, which, driven by appropriate fitness functions, can determine what are the worst-case scenarios for the DNNs under test. In addition, we will show how the lack of fidelity can be effectively addressed by leveraging Generative Adversarial Networks (GANs), to transform the data generated by simulators into realistic input images. Such images can be used both to assess the DNN performance and to retrain the DNN more effectively. The talk will finally provide insights on the results achieved in the context of the ESA projects TIA.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Supporting safety analysis of Deep Neural Networks through failure explanation<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/fabrizio-pastore\/\">Fabrizio Pastore<\/a> <mark>University of Luxemburg<\/mark><p>Deep neural networks are increasingly important in safety-and mission-critical systems, including space systems, for example, in their perception layer to analyze images. Unfortunately, there is a lack of methods to ensure the functional safety of DNN-based components, specifically we identify three major challenges with existing practices: 1) situations that are under-represented in the test set may lead to serious safety violation risks but may, however, remain unnoticed; 2) characterizing such high-risk situations is critical for safety analysis; 3) retraining DNNs to address these risks is poorly supported when causes of violations are difficult to determine. <\/p>\n<p>This lessons provides an overview of failure explanation approaches for DNNs that enable the detection of the distinct situations in which a DNN may fail and, consequently, enable the identification of countermeasures. Particular emphasis will be given to analysis pipelines that enable the identification of such situations by identifying cluster of similar failure-inducing images. They combine transfer learning, autoencoders, heatmaps of neuron relevance, dimensionality reduction techniques, and different clustering algorithms. The lesson will provide the results obtained in the context of the ESA project TIA and will include hands-on sessions to experiment with the presented solutions.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Analysis of Perception Neural Networks via Vision-Language Models<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/corina-pasarenau\/\">Corina Pasarenau<\/a> <mark>Carnegie Mellon University, NASA<\/mark><p>The analysis of Deep Neural Networks (DNNs), particularly those used as perception modules, is very challenging due to the networks&#8217; complex and opaque decision-making processes. Multi-modal Vision-Language Models (VLMs) such as CLIP offer an exciting opportunity to interpret the representation space of vision models using natural language. VLMs have been trained on a large body of images accompanied by their textual description, and are thus implicitly aware of high-level, human-understandable concepts describing the images. In this talk, we report on on-going work that seeks to leverage VLMs for the formal analysis and run-time monitoring of requirements expressed in terms of natural-language concepts, as well as debugging of perception modules.<\/p>\n<\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><h5><i>Social event<\/i><\/h5><\/td><\/tr><\/tbody><\/table><h3>May 29, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>AI for Earth Observation (AI4EO) in action: Insights and Innovations<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/cristiano-nattero\/\">Cristiano Nattero<\/a> <mark>WASDI<\/mark><p>Explore the potential and challenges of Artificial Intelligence for Earth Observation (AI4EO). This session begins with insights from WASDI, showcasing real-world examples and case studies that highlight both the successes and limitations of AI4EO applications. From innovative use cases to persistent hurdles-such as ethical concerns, inflated expectations, algorithmic hallucinations, and reliability issues-we will examine the state of the art and identify key open problems. The lecture concludes with a detailed exploration of a specific case study, offering a balanced perspective on how AI can be leveraged for Earth Observation while addressing its current constraints.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Building a Cloud-Based Downstream Earth Observation Application with WASDI<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/cristiano-nattero\/\">Cristiano Nattero<\/a> <mark>WASDI<\/mark><p>This hands-on session will guide participants through the implementation of a basic downstream Earth Observation (EO) application in the cloud using WASDI. Starting from scratch, we&#8217;ll cover the process of algorithm design, data acces, and deployment in a cloud environment. Participants will learn how to leverage WASDI&#8217;s tools to simplify development, scale processing, and prototype EO services. By the end of this session, attendees will have the foundational skills to create and deploy their own EO applications, opening the door to scalable research and service-oriented projects.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>An Overview of Anomaly Detection for Time Series<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/paul-boniol\/\">Paul Boniol<\/a> <mark>University of Paris-Descartes<\/mark><p>Anomaly detection is an important problem in data analytics with applications in many domains. In recent years, there has been an increasing interest in anomaly detection tasks applied to time series. In this talk, we take a holistic view of anomaly detection in time series, starting from the core definitions and taxonomies related to time series and anomaly types, to an extensive description of the anomaly detection methods proposed by different communities in the literature. We will then present new benchmarks capturing diverse domains and applications for the purpose of evaluating anomaly detection methods. We will then conclude on Ensembling and Model Selection for time series anomaly detection, discussing different strategies applicable to automatically selecting the appropriate methods for a specific time series.<\/p>\n<\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>A deep dive into time series anomaly detection evaluation measures<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/paul-boniol\/\">Paul Boniol<\/a> <mark>University of Paris-Descartes<\/mark><p>Anomaly detection is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains where anomaly detection mainly focuses on point-based anomalies (i.e., outliers in standalone observations), anomaly detection for time series is also concerned with range-based anomalies (i.e., outliers spanning multiple observations). Nevertheless, it is common to use traditional point-based information retrieval measures, such as Precision, Recall, and Fscore, to assess the quality of methods by thresholding the anomaly score to mark each point as an anomaly or not. However, mapping discrete labels into continuous data introduces unavoidable shortcomings, complicating the evaluation of range-based contextual and collective anomalies. In this talk, we will dive into the existing evaluation measures to properly compare anomaly detection methods for time series. Finally, we will discuss how evaluation measures can be compared and evaluated.<\/p>\n<\/td><\/tr><\/tbody><\/table><h3>May 30, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Automating spaceborne Earth observation data exploitation through deep learning: current status and future perspectives<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/filippo-britti\/\">Filippo Britti<\/a> <mark>e-Geos<\/mark><p>Thanks to the recent progress in artificial intelligence and the continuous improvements on parallel computing capabilities, the application of computer vision methodologies within the satellite remote sensing field is becoming increasingly a reality for Earth observation (EO) purposes. The present talk will therefore aim at showing current status of principal deep-learning based solutions for traditional EO tasks such as object detection and classification, data preparation, land-cover classification and physical-biological parameters regression. Practical examples coming from both experimental and operational activities will be provided, leveraging on diverse satellite Earth observation technologies. Further, an outlook on future perspectives and impacts of such technologies will be discussed.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>ORBIT: Web application for research and planning of potential satellite acquisitions for detection<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/giusy-cappelluti\/\">Giusy Cappelluti<\/a> <mark>Fondazione CIMA<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/alessandro-cottino\/\">Alessandro Cottino<\/a> <mark>Fondazione CIMA<\/mark>Satellite data is very useful for evaluating the effects on the ground in the event of extreme events, such as floods, which increasingly affect our territory.<br \/>\nUsually, to search for possible useful acquisitions for emergency management and planning of interventions to help the populations of areas flooded or affected by an extreme event, it is necessary to access various portals and then summarize the best opportunities.<br \/>\nORBIT allows us to carry out a search for the main satellite acquisitions available (e.g. Sentinel, COSMO SkyMed) on a particular geographical area selected by the user in order to plan those of greatest interest for the case study.\n<p>Students will be invited to practice with the ORBIT tool starting from a case study.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Use of high-resolution remote sensing data for mapping the extent of water reservoirs and flooded areas: basic principles and applications<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/luca-pulvirenti\/\">Luca Pulvirenti<\/a> <mark>Fondazione CIMA<\/mark><p>Climate change is increasingly manifested by global increases in the frequency and magnitude of hydrological extremes (i.e., floods and droughts), with severe impacts on economies, livelihoods, and the environment. In a warming climate, both floods and droughts are likely to intensify (i.e., increasing in their duration, frequency, and severity). This can increase the likelihood of rapid shifts or overlap between these opposite hydrological extremes. For instance, in Northern Italy, a big flood affected Emilia-Romagna in May 2023 after the second winter drought in two years. The same region was again affected by floods in September and October 2024, while at the same time, southern Italy was affected by a severe drought. The latter, in turn, caused a significant shrinking of some lakes used for water supply of several municipalities.<\/p>\n<p>High resolution remote sensing data provide important information for flood mapping and lake extent monitoring. Concerning flood mapping, they allow emergency managers to rapidly have a synoptic overview of the extent of the inundation, as well as to monitor its temporal evolution. In this context, synthetic aperture radar (SAR) sensors represent the most commonly used instruments, because they can provide data both day and night and in almost any meteorological conditions.<\/p>\n<p>As for the monitoring of the extent of the water reservoirs, also optical data can be considered as a useful source of information. While they are often unsuitable for inundation mapping because of the frequent cloud cover associated with these disasters, they can be profitably exploited for a long-term lake extent monitoring (e.g., on a monthly basis), at regional or even national scale. This activity is helpful to identify possible situations of water scarcity.<\/p>\n<p>The presentation will illustrate the basic principles of remote sensing of water surfaces from optical and SAR data and will show some applications performed in collaboration with the Italian Department of Civil Protection. For SAR data, the presentation will show examples of the use of both Sentinel-1 and COSMO-SkyMed (1st and 2nd generation). Regarding optical data, the presentation will focus on Sentinel-2.<\/p>\n<\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Downscaling satellite precipitation fields using convolutional neural networks<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/edoardo-raparelli\/\">Edoardo Raparelli<\/a> <mark>University of L&#8217;Aquila<\/mark><p>Precipitation data derived from satellite observations is a crucial resource for hydrology, meteorology, and climate research, particularly in regions with limited ground-based monitoring. However, satellite precipitation estimates are often limited in spatial resolution, posing challenges for local-scale applications such as flood forecasting, water resource management, avalanche danger forecasting and agricultural planning. To address this, convolutional neural networks (CNNs) have emerged as a powerful tool for downscaling coarse-resolution images to high-resolution grids, enhancing its utility for fine-scale analyses. This lecture reviews the potential of CNN-based downscaling techniques for increasing spatial resolution of satellite derived precipitation data. We discuss the architecture of CNN models, their training processes on diverse datasets, and their ability to capture spatial patterns and intricate features within precipitation fields. Results demonstrate that CNN-based downscaling improves the spatial detail and accuracy of satellite derived precipitation data, underscoring the value of combining satellite data with advanced machine learning techniques to overcome observational limitations in Earth sciences.<\/p>\n<\/td><\/tr><\/tbody><\/table>\n<h3>\n\t\tRobotics for Space Applications\n\t<\/h3>\n\tWeek #1\u00a0\u00a0 | \u00a0 <strong>Monday, May 12 \u2192 Friday, May 16<\/strong> \u00a0 |\u00a0\u00a0 Auditorium &#8211; Palazzo del Rettorato, GSSI.\nThis module will discuss aspects of programming and safety strategies for space exploration robots. Additionally, use cases of robots in space will be considered, both for exploration and for maintenance.\n\t\t\t<a href=\"\" target=\"_self\">\n\t\t\t\t\t\t\tShow schedule details\n\t\t\t<\/a>\n<hr \/><h5>The course schedules are in the process of being finalized and are subject to minor changes.<\/h5><hr \/><h3>May 26, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><h5><i>Registration + Keynote: AI and the infinite: the intersection of AI and Space ventures<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/elena-grifoni-winters\/\">Elena Grifoni Winters<\/a> <mark>GSSI<\/mark><\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Trustworthiness and explainability in AI for Space application<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/arturo-intelisano\/\">Arturo Intelisano<\/a> <mark>Thales Alenia Space Italia<\/mark><p>As artificial intelligence (AI) increasingly plays a pivotal role in space applications-ranging from satellite operations to autonomous navigation and data analysis-it is crucial to establish a trustworthy framework for its deployment. Trustworthiness in AI is essential to ensure reliability, safety, and integrity in space missions. To achieve this, identifying and defining specific metrics based on explainable mechanisms and theories is fundamental. This approach facilitates transparency in AI decision-making, particularly when using complex models like neural networks. By focusing on interpretability and accountability, researchers can enhance the predictability of AI systems, allowing stakeholders to understand how decisions are made and ensuring that AI solutions align with mission objectives while mitigating risks.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><h5><i>AI\/ML for safety-critical software: the case of the space domain <\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-basciani\/\">Francesco Basciani<\/a> <mark>GSSI<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/alberto-petrucci\/\">Alberto Petrucci<\/a> <mark>Thales Alenia Space Italia<\/mark><\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>New generation of virtualization infrastructure in space<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/andrea-danieli\/\">Andrea Danieli<\/a> <mark>Telespazio<\/mark>The course explores the evolution of virtualization infrastructures applied to space systems, focusing on the adoption of containerization technologies and orchestration tools to manage highly dynamic environments and limited resources.<br \/>\nParticipants will discover how the use of containers (e.g. Docker) allows to isolate critical applications, improving security and operational flexibility in space mission conditions.<br \/>\nStrategies for orchestrating distributed containers (with tools such as Kubernetes) will be analyzed, in order to ensure effective resource management, automation of deployment processes and system resilience in the presence of peculiar challenges such as environmental alterations and energy limitations.<br \/>\nThe training path offers an integrated overview of the design and implementation of intelligent virtual infrastructures for space environments.<br \/>\nThe course also delves into aspects such as workload sizing, performance monitoring and integration with hybrid cloud systems, providing participants with the skills needed to innovate onboard process management and resilience-oriented network configuration.<br \/>\nIdeal for engineers, researchers and professionals in the aerospace and IT sectors, this course prepares specialists to face the challenges of digitalization and to design solutions capable of supporting next-generation space missions, ensuring safety, scalability and operational efficiency.\n<\/td><\/tr><\/tbody><\/table><h3>May 27, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Introduction to AI in Space Safety and Sustainability<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/federica-paganelli\/\">Federica Paganelli<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-evangelisti\/\">Francesco Evangelisti<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/tobia-giani\/\">Tobia Giani<\/a> <mark>Aiko Space<\/mark><p>This session explores how Artificial Intelligence is shaping the future of space safety and sustainability. Topics include the use of AI for collision avoidance, debris management, and autonomous decision-making in satellite operations. Practical use cases will demonstrate how AI enhances operational efficiency, reduces human intervention, and contributes to sustainable space activities.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Applying AI to Mission Design: Working Groups<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/federica-paganelli\/\">Federica Paganelli<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-evangelisti\/\">Francesco Evangelisti<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/tobia-giani\/\">Tobia Giani<\/a> <mark>Aiko Space<\/mark><p>Participants will collaborate in groups to design mission scenarios that leverage AI for sustainability-focused objectives. These include designing autonomous collision-avoidance systems, optimizing satellite constellations, and planning debris mitigation strategies. Facilitators will guide discussions and provide examples of AI solutions in real-world applications.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;13:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>13:00&nbsp;&#8211;&nbsp;14:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Introduction to Machine Learning for Onboard Data Analysis in Earth Observation<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-rossi\/\">Francesco Rossi<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/armando-la-rocca\/\">Armando La Rocca<\/a> <mark>Aiko Space<\/mark><p>This session provides an overview of machine learning (ML) techniques for real-time data processing onboard satellites. Key topics include the use of ML for Earth observation data, such as anomaly detection, environmental monitoring, and rapid disaster assessment. Participants will gain insights into how onboard AI models can operate under constraints like limited computational power and latency.<\/p>\n<\/td><\/tr><tr><td>14:30&nbsp;&#8211;&nbsp;15:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>15:00&nbsp;&#8211;&nbsp;16:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Hands-On: Implementing a Neural Network-Based Classification Algorithm<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/francesco-rossi\/\">Francesco Rossi<\/a> <mark>Aiko Space<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/armando-la-rocca\/\">Armando La Rocca<\/a> <mark>Aiko Space<\/mark><p>In this hands-on session, participants will implement a neural network to classify Earth observation data. The session covers essential steps, from dataset preprocessing to model training and performance evaluation. Examples include classifying land use, detecting changes in vegetation, and identifying urban growth patterns. The workshop will emphasize practical implementation and provide participants with tools to adapt the algorithm for various use cases.<\/p>\n<\/td><\/tr><tr><td>16:30&nbsp;&#8211;&nbsp;18:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>A trillion dollars economy? Space economy fundamentals trends and externalities<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/elena-grifoni-winters\/\">Elena Grifoni Winters<\/a> <mark>GSSI<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/pierre-lionnet\/\">Pierre Lionnet<\/a> <mark>Eurospace<\/mark><p>The space economy has now become a subject of discussion in mainstream media and political circles, especially since the publication by financial institutions and major consultancies of prospects for its exponential growth (the infamous Trillion Dollar Space Economy projections) and the increased focus given to the &#8216;newspace&#8217; and private equity investments in the space sector. In this lecture we will look at an economist perspective on space markets and industrial structures, focusing on infrastructure development and financing, and delivering a data-based analysis of major trends affecting the space sector in the past decades, inlcuding actual, or perceived, paradigm changes. The lecture will review the variety of good (and not so good) data sources and methodologies to accurately measure the economic outturn of space activities, stressing the notions of markets (for infrastructure and services) and externalities (positive and negative) and will deliver an organised toolbox to assess the economics of space activities worldwide.<\/p>\n<\/td><\/tr><\/tbody><\/table><h3>May 28, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Automated testing of deep neural networks for computer vision tasks in space<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/fabrizio-pastore\/\">Fabrizio Pastore<\/a> <mark>University of Luxemburg<\/mark><p>In safety- and mission-critical space systems such as rovers and satellites, Deep Neural Networks (DNNs) are becoming a key component for computer vision tasks, particularly object detection and autonomous navigation. However, since the DNN behavior cannot be assessed through code inspection and analysis, test automation has become an essential activity to gain confidence in the reliability of DNNs. <\/p>\n<p>Since DNNs for computer vision operate on large input spaces (e.g., any possible landscape for a Mars rover) traditional DNN testing approaches leveraging data sets with a limited number of pictures collected in the field might not enable the detection of all DNN limitations. For this reason, simulators, which may generate potentially infinite number of diverse images along with additional information to be used as ground-truth, have become a valid tool for DNN testing. But generating random images might not be the most effective solution.<\/p>\n<p>This talk provides an overview of recent research approaches combining simulators with evolutionary search algorithms, which, driven by appropriate fitness functions, can determine what are the worst-case scenarios for the DNNs under test. In addition, we will show how the lack of fidelity can be effectively addressed by leveraging Generative Adversarial Networks (GANs), to transform the data generated by simulators into realistic input images. Such images can be used both to assess the DNN performance and to retrain the DNN more effectively. The talk will finally provide insights on the results achieved in the context of the ESA projects TIA.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Supporting safety analysis of Deep Neural Networks through failure explanation<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/fabrizio-pastore\/\">Fabrizio Pastore<\/a> <mark>University of Luxemburg<\/mark><p>Deep neural networks are increasingly important in safety-and mission-critical systems, including space systems, for example, in their perception layer to analyze images. Unfortunately, there is a lack of methods to ensure the functional safety of DNN-based components, specifically we identify three major challenges with existing practices: 1) situations that are under-represented in the test set may lead to serious safety violation risks but may, however, remain unnoticed; 2) characterizing such high-risk situations is critical for safety analysis; 3) retraining DNNs to address these risks is poorly supported when causes of violations are difficult to determine. <\/p>\n<p>This lessons provides an overview of failure explanation approaches for DNNs that enable the detection of the distinct situations in which a DNN may fail and, consequently, enable the identification of countermeasures. Particular emphasis will be given to analysis pipelines that enable the identification of such situations by identifying cluster of similar failure-inducing images. They combine transfer learning, autoencoders, heatmaps of neuron relevance, dimensionality reduction techniques, and different clustering algorithms. The lesson will provide the results obtained in the context of the ESA project TIA and will include hands-on sessions to experiment with the presented solutions.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Analysis of Perception Neural Networks via Vision-Language Models<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/corina-pasarenau\/\">Corina Pasarenau<\/a> <mark>Carnegie Mellon University, NASA<\/mark><p>The analysis of Deep Neural Networks (DNNs), particularly those used as perception modules, is very challenging due to the networks&#8217; complex and opaque decision-making processes. Multi-modal Vision-Language Models (VLMs) such as CLIP offer an exciting opportunity to interpret the representation space of vision models using natural language. VLMs have been trained on a large body of images accompanied by their textual description, and are thus implicitly aware of high-level, human-understandable concepts describing the images. In this talk, we report on on-going work that seeks to leverage VLMs for the formal analysis and run-time monitoring of requirements expressed in terms of natural-language concepts, as well as debugging of perception modules.<\/p>\n<\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><h5><i>Social event<\/i><\/h5><\/td><\/tr><\/tbody><\/table><h3>May 29, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>AI for Earth Observation (AI4EO) in action: Insights and Innovations<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/cristiano-nattero\/\">Cristiano Nattero<\/a> <mark>WASDI<\/mark><p>Explore the potential and challenges of Artificial Intelligence for Earth Observation (AI4EO). This session begins with insights from WASDI, showcasing real-world examples and case studies that highlight both the successes and limitations of AI4EO applications. From innovative use cases to persistent hurdles-such as ethical concerns, inflated expectations, algorithmic hallucinations, and reliability issues-we will examine the state of the art and identify key open problems. The lecture concludes with a detailed exploration of a specific case study, offering a balanced perspective on how AI can be leveraged for Earth Observation while addressing its current constraints.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Building a Cloud-Based Downstream Earth Observation Application with WASDI<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/cristiano-nattero\/\">Cristiano Nattero<\/a> <mark>WASDI<\/mark><p>This hands-on session will guide participants through the implementation of a basic downstream Earth Observation (EO) application in the cloud using WASDI. Starting from scratch, we&#8217;ll cover the process of algorithm design, data acces, and deployment in a cloud environment. Participants will learn how to leverage WASDI&#8217;s tools to simplify development, scale processing, and prototype EO services. By the end of this session, attendees will have the foundational skills to create and deploy their own EO applications, opening the door to scalable research and service-oriented projects.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>An Overview of Anomaly Detection for Time Series<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/paul-boniol\/\">Paul Boniol<\/a> <mark>University of Paris-Descartes<\/mark><p>Anomaly detection is an important problem in data analytics with applications in many domains. In recent years, there has been an increasing interest in anomaly detection tasks applied to time series. In this talk, we take a holistic view of anomaly detection in time series, starting from the core definitions and taxonomies related to time series and anomaly types, to an extensive description of the anomaly detection methods proposed by different communities in the literature. We will then present new benchmarks capturing diverse domains and applications for the purpose of evaluating anomaly detection methods. We will then conclude on Ensembling and Model Selection for time series anomaly detection, discussing different strategies applicable to automatically selecting the appropriate methods for a specific time series.<\/p>\n<\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>A deep dive into time series anomaly detection evaluation measures<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/paul-boniol\/\">Paul Boniol<\/a> <mark>University of Paris-Descartes<\/mark><p>Anomaly detection is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains where anomaly detection mainly focuses on point-based anomalies (i.e., outliers in standalone observations), anomaly detection for time series is also concerned with range-based anomalies (i.e., outliers spanning multiple observations). Nevertheless, it is common to use traditional point-based information retrieval measures, such as Precision, Recall, and Fscore, to assess the quality of methods by thresholding the anomaly score to mark each point as an anomaly or not. However, mapping discrete labels into continuous data introduces unavoidable shortcomings, complicating the evaluation of range-based contextual and collective anomalies. In this talk, we will dive into the existing evaluation measures to properly compare anomaly detection methods for time series. Finally, we will discuss how evaluation measures can be compared and evaluated.<\/p>\n<\/td><\/tr><\/tbody><\/table><h3>May 30, 2025<\/h3><table><tbody><tr><td>08:30&nbsp;&#8211;&nbsp;10:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Automating spaceborne Earth observation data exploitation through deep learning: current status and future perspectives<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/filippo-britti\/\">Filippo Britti<\/a> <mark>e-Geos<\/mark><p>Thanks to the recent progress in artificial intelligence and the continuous improvements on parallel computing capabilities, the application of computer vision methodologies within the satellite remote sensing field is becoming increasingly a reality for Earth observation (EO) purposes. The present talk will therefore aim at showing current status of principal deep-learning based solutions for traditional EO tasks such as object detection and classification, data preparation, land-cover classification and physical-biological parameters regression. Practical examples coming from both experimental and operational activities will be provided, leveraging on diverse satellite Earth observation technologies. Further, an outlook on future perspectives and impacts of such technologies will be discussed.<\/p>\n<\/td><\/tr><tr><td>10:00&nbsp;&#8211;&nbsp;10:30<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>10:30&nbsp;&#8211;&nbsp;12:00<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>ORBIT: Web application for research and planning of potential satellite acquisitions for detection<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/giusy-cappelluti\/\">Giusy Cappelluti<\/a> <mark>Fondazione CIMA<\/mark>, <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/alessandro-cottino\/\">Alessandro Cottino<\/a> <mark>Fondazione CIMA<\/mark>Satellite data is very useful for evaluating the effects on the ground in the event of extreme events, such as floods, which increasingly affect our territory.<br \/>\nUsually, to search for possible useful acquisitions for emergency management and planning of interventions to help the populations of areas flooded or affected by an extreme event, it is necessary to access various portals and then summarize the best opportunities.<br \/>\nORBIT allows us to carry out a search for the main satellite acquisitions available (e.g. Sentinel, COSMO SkyMed) on a particular geographical area selected by the user in order to plan those of greatest interest for the case study.\n<p>Students will be invited to practice with the ORBIT tool starting from a case study.<\/p>\n<\/td><\/tr><tr><td>12:00&nbsp;&#8211;&nbsp;14:00<\/td><td><h5><i>Lunch break<\/i><\/h5><\/td><\/tr><tr><td>14:00&nbsp;&#8211;&nbsp;15:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Use of high-resolution remote sensing data for mapping the extent of water reservoirs and flooded areas: basic principles and applications<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/luca-pulvirenti\/\">Luca Pulvirenti<\/a> <mark>Fondazione CIMA<\/mark><p>Climate change is increasingly manifested by global increases in the frequency and magnitude of hydrological extremes (i.e., floods and droughts), with severe impacts on economies, livelihoods, and the environment. In a warming climate, both floods and droughts are likely to intensify (i.e., increasing in their duration, frequency, and severity). This can increase the likelihood of rapid shifts or overlap between these opposite hydrological extremes. For instance, in Northern Italy, a big flood affected Emilia-Romagna in May 2023 after the second winter drought in two years. The same region was again affected by floods in September and October 2024, while at the same time, southern Italy was affected by a severe drought. The latter, in turn, caused a significant shrinking of some lakes used for water supply of several municipalities.<\/p>\n<p>High resolution remote sensing data provide important information for flood mapping and lake extent monitoring. Concerning flood mapping, they allow emergency managers to rapidly have a synoptic overview of the extent of the inundation, as well as to monitor its temporal evolution. In this context, synthetic aperture radar (SAR) sensors represent the most commonly used instruments, because they can provide data both day and night and in almost any meteorological conditions.<\/p>\n<p>As for the monitoring of the extent of the water reservoirs, also optical data can be considered as a useful source of information. While they are often unsuitable for inundation mapping because of the frequent cloud cover associated with these disasters, they can be profitably exploited for a long-term lake extent monitoring (e.g., on a monthly basis), at regional or even national scale. This activity is helpful to identify possible situations of water scarcity.<\/p>\n<p>The presentation will illustrate the basic principles of remote sensing of water surfaces from optical and SAR data and will show some applications performed in collaboration with the Italian Department of Civil Protection. For SAR data, the presentation will show examples of the use of both Sentinel-1 and COSMO-SkyMed (1st and 2nd generation). Regarding optical data, the presentation will focus on Sentinel-2.<\/p>\n<\/td><\/tr><tr><td>15:30&nbsp;&#8211;&nbsp;16:00<\/td><td><h5><i>Coffee break<\/i><\/h5><\/td><\/tr><tr><td>16:00&nbsp;&#8211;&nbsp;17:30<\/td><td><button title=\"Abstract\"><\/button><\/td><td><h5><i>Downscaling satellite precipitation fields using convolutional neural networks<\/i><\/h5><a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/speaker\/edoardo-raparelli\/\">Edoardo Raparelli<\/a> <mark>University of L&#8217;Aquila<\/mark><p>Precipitation data derived from satellite observations is a crucial resource for hydrology, meteorology, and climate research, particularly in regions with limited ground-based monitoring. However, satellite precipitation estimates are often limited in spatial resolution, posing challenges for local-scale applications such as flood forecasting, water resource management, avalanche danger forecasting and agricultural planning. To address this, convolutional neural networks (CNNs) have emerged as a powerful tool for downscaling coarse-resolution images to high-resolution grids, enhancing its utility for fine-scale analyses. This lecture reviews the potential of CNN-based downscaling techniques for increasing spatial resolution of satellite derived precipitation data. We discuss the architecture of CNN models, their training processes on diverse datasets, and their ability to capture spatial patterns and intricate features within precipitation fields. Results demonstrate that CNN-based downscaling improves the spatial detail and accuracy of satellite derived precipitation data, underscoring the value of combining satellite data with advanced machine learning techniques to overcome observational limitations in Earth sciences.<\/p>\n<\/td><\/tr><\/tbody><\/table><h2>\n\t\tSPEAKERS\n\t<\/h2>\n\t<p>Our lecturers are recognized experts in the topics of the three modules with exceptional teaching abilities. The SPACERAISE program offers a well-balanced mix of academic and industrial contributions. The academic lecturers will present overviews of their research areas, covering the state of the art and highlighting emerging trends and challenges. The industrial lecturers will focus on practical applications in industrial contexts, sharing the results achieved in real-world scenarios.<\/p>\n<h2>\n\t\tREGISTRATION AND SCHOLARSHIPS\n\t<\/h2>\n\t<p>To attend SPACERAISE it&#8217;s required to apply using a dedicated form to upload resume and personal information. Applicants must submit their application by January 31st, 2025. Based on review of the uploaded info, SPACERAISE will determine and communicate effective registration to successful applicants and, in selected cases, confer scholarships.<\/p>\n<p>The criteria for awarding the scholarship can be found <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/11\/SPACERAISE-scholarship-assignment-criteria.pdf\" target=\"_blank\" rel=\"noopener\">here<\/a>.<\/p>\n<p>Successful applicants will be able to attend all scientific sessions, social activities, and coffee breaks with no registration fee to pay. Additionally, SPACERAISE is pleased to offer a limited number of merit-based scholarships.<\/p>\n<p>The scholarship is not intended to cover all travel and accommodation expenses, but rather to provide financial support to help participants with these costs, which remain their responsibility.<\/p>\n<b>Important Dates<br \/>\n<\/b>Application deadline: <b>January 31, 2025<\/b><br \/>\nNotifications of acceptance, rejection, and scholarships assignment: <b>February 12, 2025<\/b>\n\t<p>If additional places become available, registration will reopen by the end of February. Applicants admitted after January 31 will not be eligible for the scholarship selection.<\/p>\n\t\t\t<a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/register\/\" target=\"_self\">\n\t\t\t\t\t\t\tRegistration is closed\n\t\t\t<\/a>\n\t<h1>LOCATIONS<\/h1>\n<strong>Week #1<br \/>\n<\/strong>Auditorium &#8211; Palazzo del Rettorato, GSSI\n<strong>Week #2<br \/>\n<\/strong>Sala Ipogea and Giardino d&#8217;Inverno &#8211; Palazzo dell&#8217;Emiciclo, Regione Abruzzo.<br \/>\n\n<strong>Week #3<\/strong><br \/>\nAuditorium &#8211; Palazzo del Rettorato, GSSI\n<p>Need directions? Find <a href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/how-to-reach\/\">how to reach<\/a>.<\/p>\n<h2>\n\t\tORGANIZERS\n\t<\/h2>\n\t\t\t\t<a href=\"https:\/\/www.gransassotech.org\/\"  target=\"_blank\" rel=\"noopener\"  itemprop=\"url\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/10\/gransasso_tech_logo_1.svg\" alt=\"Gran Sasso Tech\" itemprop=\"image\" height=\"150\" width=\"850\" title=\"Gran Sasso Tech\" onerror=\"this.style.display='none'\"  \/>\n\t\t\t\t<\/a>\n\t\t\t\t<a href=\"https:\/\/www.gssi.it\/\"  target=\"_blank\" rel=\"noopener\"  itemprop=\"url\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/10\/Gran-Sasso-Science-Institute.png\" alt=\"Gran Sasso Science Institute\" itemprop=\"image\" height=\"118\" width=\"300\" title=\"Gran Sasso Science Institute\" onerror=\"this.style.display='none'\"  \/>\n\t\t\t\t<\/a>\n\t\t\t\t<a href=\"https:\/\/matisse-kdt.eu\/\"  target=\"_blank\" rel=\"noopener\"  itemprop=\"url\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/12\/matisse-logotype.svg\" alt=\"MATISSE\" itemprop=\"image\" height=\"612\" width=\"1000\" title=\"MATISSE\" onerror=\"this.style.display='none'\"  \/>\n\t\t\t\t<\/a>\n<h6>\n\t\tPATRONAGE\n\t<\/h6>\n\t\t\t\t<a href=\"https:\/\/www.regione.abruzzo.it\/\"  target=\"_blank\" rel=\"noopener\"  itemprop=\"url\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2025\/03\/LogoConsiglioAGG.2024_trasp-300x183.png\" alt=\"Regione Abruzzo - Consiglio Regionale\" itemprop=\"image\" height=\"183\" width=\"300\" title=\"Regione Abruzzo - Consiglio Regionale\" onerror=\"this.style.display='none'\"  \/>\n\t\t\t\t<\/a>\n<h6>\n\t\tSPONSOR\n\t<\/h6>\n\t\t\t\t<a href=\"https:\/\/safi3.it\/\"  target=\"_blank\" rel=\"noopener\"  itemprop=\"url\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2025\/04\/Logo-SAFI3-300x117.png\" alt=\"SAFI3 - INNOVATIVA INTERDISCIPLINARE INTERNAZIONALE\" itemprop=\"image\" height=\"117\" width=\"300\" title=\"SAFI3 - INNOVATIVA INTERDISCIPLINARE INTERNAZIONALE\" onerror=\"this.style.display='none'\"  \/>\n\t\t\t\t<\/a>\n\t\t\t\t<img decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/plugins\/bb-plugin\/img\/pixel.png\" alt=\"\" itemprop=\"image\" onerror=\"this.style.display='none'\"  \/>\n<h6>\n\t\tADVERTISEMENT\n\t<\/h6>\n\t\t\t\t<a href=\"https:\/\/www.summerschoolsineurope.eu\/\"  target=\"_blank\" rel=\"noopener\"  itemprop=\"url\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/12\/Summer-Schools-in-Europe.svg\" alt=\"Summer Schools in Europe\" itemprop=\"image\" height=\"76\" width=\"351\" title=\"Summer Schools in Europe\" onerror=\"this.style.display='none'\"  \/>\n\t\t\t\t<\/a>\n\t\t<h3>Need More Information?<\/h3>\n\t\tHave a question or need more details?<br \/>\nDon&#8217;t hesitate to reach out to us.<br \/>\nOur team is ready to assist you.\n\t\t\t<a href=\"mailto:spaceraise@gransassotech.org\" target=\"_self\">\n\t\t\t\t\t\t\tContact Us\n\t\t\t\t\t<\/a>\n\n","protected":false},"excerpt":{"rendered":"<p>Week #1 12 \u2192 16 May 2025 Robotics for Space Applications Week #2 19 \u2192 23 May 2025 Digital Twins for Space Manufacturing and Satellites Week #3 26 \u2192 30 May 2025 Artificial Intelligence for Aerospace PROJECT SPACERAISE consists of an intensive training program with a high technological and scientific content aimed at young talents&hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-8","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.1.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Homepage - SPACERAISE<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Homepage - SPACERAISE\" \/>\n<meta property=\"og:description\" content=\"Week #1 12 \u2192 16 May 2025 Robotics for Space Applications Week #2 19 \u2192 23 May 2025 Digital Twins for Space Manufacturing and Satellites Week #3 26 \u2192 30 May 2025 Artificial Intelligence for Aerospace PROJECT SPACERAISE consists of an intensive training program with a high technological and scientific content aimed at young talents&hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/\" \/>\n<meta property=\"og:site_name\" content=\"SPACERAISE\" \/>\n<meta property=\"article:modified_time\" content=\"2025-10-10T08:18:47+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/spaceraise.academy\/pasteditions\/2025\/wp-content\/uploads\/2024\/10\/gransasso_tech_logo_1.svg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"46 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/spaceraise.academy\/pasteditions\/2025\/\",\"url\":\"https:\/\/spaceraise.academy\/pasteditions\/2025\/\",\"name\":\"Homepage - 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