{"id":215,"date":"2025-10-08T09:17:42","date_gmt":"2025-10-08T09:17:42","guid":{"rendered":"https:\/\/www.ieeesmc.org\/cai-2026\/?page_id=215"},"modified":"2025-10-20T08:29:48","modified_gmt":"2025-10-20T08:29:48","slug":"special-sessions","status":"publish","type":"page","link":"https:\/\/www.ieeesmc.org\/cai-2026\/special-sessions\/","title":{"rendered":"Special Sessions"},"content":{"rendered":"<p>The following <strong>Special Sessions<\/strong> will be carried out at CAI 2026:<\/p>\n<ol>\n<li><a href=\"#ss1\"><strong title=\"Special Session Code: SS1-MULTI\">SS1-MULTI:<\/strong> (Multi-)Agentic AI in Action: Real-World Applications of Contemporary Multi-Agent Systems<\/a><\/li>\n<li><a href=\"#ss2\"><strong title=\"Special Session Code: SS2-LLMS\">SS2-LLMS:<\/strong> Emerging Trends in Data, Web, and Social Media Analysis and Generation with LLMs<\/a><\/li>\n<li><a href=\"#ss3\"><strong title=\"Special Session Code: SS3-SPACE\">SS3-SPACE:<\/strong> AI for Space Exploration<\/a><\/li>\n<li><a href=\"#ss4\"><strong title=\"Special Session Code: SS4-FINANCE\">SS4-FINANCE:<\/strong> Foundation and Emerging AI Models for Applications in Finance, Cyber Security, and Life Sciences<\/a><\/li>\n<li><a href=\"#ss5\"><strong title=\"Special Session Code: SS5-ETHIC\">SS5-ETHIC:<\/strong> From the Streets to the Systems: A Journey Through Ethical AI, Workforce Justice, and Radical Inclusion<\/a><\/li>\n<li><a href=\"#ss6\"><strong title=\"Special Session Code: SS6-FIRE\">SS6-FIRE:<\/strong> FIRE-AI: Federated Intelligence for Responsible, Evolving AI<\/a><\/li>\n<li><a href=\"#ss7\"><strong title=\"Special Session Code: SS7-DRONES\">SS7-DRONES:<\/strong> AI-Enabled Drones for Environmental Monitoring and Sustainable Land Management<\/a><\/li>\n<li><a href=\"#ss8\"><strong title=\"Special Session Code: SS8-INDUSTRY\">SS8-INDUSTRY:<\/strong> Industry and AI<\/a><\/li>\n<\/ol>\n<hr \/>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.4em;font-weight: bold\" id=\"ss1\"><em title=\"Special Session Code: SS1-MULTI\">SS1-MULTI:<\/em> (Multi-)Agentic AI in Action: Real-World Applications of Contemporary Multi-Agent Systems<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Proposers<\/p>\n<p>COSTIN B\u0102DIC\u0102, MARIA GANZHA, MIRJANA IVANOVI\u0106 and MARCIN PAPRZYCKI<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Session Code<\/p>\n<p>Please use the following code when submitting your paper to this session: <strong title=\"Special Session Code: SS1-MULTI\">SS1-MULTI<\/strong><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Rationale and Vision<\/p>\n<p>As highlighted in recent works (<a href=\"#ss1refs\">[1]<\/a>, <a href=\"#ss1refs\">[2]<\/a>), the field of intelligent agents and multi-agent systems is at a fascinating inflection point. Here, two powerful paradigms can be distinguished: the structured, verifiable world of classic Multi-Agent Systems (MAS) and the fluid, semantically rich world of LLM-driven generative agents (Agentic Systems). The most exciting breakthroughs are now happening at their intersection, where these paradigms are not just compared, but actively combined to solve real-world problems.<\/p>\n<p>This special session moves beyond theoretical debates to showcase concrete, impactful use cases and applications. We aim to gather researchers and practitioners who are deploying the next generation of multi-agent systems to tackle complex challenges. The focus is on empirical results, novel use-cases, and the practical lessons learned from building and deploying agentic AI &#8220;in the wild.&#8221; We are particularly interested in systems that strategically blend the strengths of classic MAS (e.g., explicit coordination, formal negotiation) with the advanced reasoning and communication capabilities of foundation models.<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">List of Topics<\/p>\n<p>This session invites submissions demonstrating tangible applications of (multi-)agentic AI. The emphasis<br \/>\nis on systems, results, and real-world impact.<\/p>\n<ul>\n<li>Hybrid Agent Systems in Practice: Case studies of systems successfully integrating classic agent architectures (e.g., BDI, auctions, argumentation) with LLM-based components for enhanced performance.<\/li>\n<li>Agents for Science and Engineering: Applications in automated scientific discovery, lab automation, complex system design, healthcare &amp; life sciences, and AI-assisted software development.<\/li>\n<li>Autonomous Business and Economic Systems: Agent-based models for supply chain optimization, automated trading, and decentralized autonomous organizations (DAOs).<\/li>\n<li>Human-Agent Collectives: Systems and interfaces for effective human-AI teaming in areas like creative design, strategic planning, and complex data analysis.<\/li>\n<li>Generative Agents for Social Simulation: Using LLM-powered agents to create high-fidelity simulations of social, cultural, and economic dynamics for research and policy-making.<\/li>\n<li>Agents in Interactive Entertainment: Applications in gaming, interactive narratives, education, and the creation of dynamic, believable non-player characters (NPCs).<\/li>\n<li>Evaluation of Deployed Agent Systems: Methodologies and metrics for evaluating agent performance,robustness, and safety in real-world, uncontrolled environments.<\/li>\n<\/ul>\n<p id=\"ss1refs\">[1] Costin B\u0103dic\u0103, Amelia B\u0103dic\u0103, Maria Ganzha, Mirjana Ivanovi\u0107, Marcin Paprzycki, Dan Seli\u015fteanu and Zofia Wrona, Contemporary Agent Technology: LLM-Driven Advancements vs Classic Multi-Agent Systems, arXiv, 2509.02515 [cs.MA], 2025. <a href=\"https:\/\/arxiv.org\/abs\/2509.02515\">https:\/\/arxiv.org\/abs\/2509.02515<\/a><\/p>\n<p>[2] Zofia Wrona, Maria Ganzha, Marcin Paprzycki, Wies\u0142aw Paw\u0142owski, Angelo Ferrando, Giacomo Cabri, and Costin B\u0103dic\u0103. Comparison of Multi-Agent Platform Usability for Industrial-Grade Applications. Applied Sciences 14 (22): 10124, 2024. <a href=\"https:\/\/doi.org\/10.3390\/app142210124\">https:\/\/doi.org\/10.3390\/app142210124<\/a><\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.4em;font-weight: bold\" id=\"ss2\"><em title=\"Special Session Code: SS2-LLMS\">SS2-LLMS:<\/em> Emerging Trends in Data, Web, and Social Media Analysis and Generation with LLMs<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Proposers<\/p>\n<p><a href=\"mailto:mdruiz@decsai.ugr.es\">M. Dolores Ruiz<\/a> and <a href=\"mailto:mbautis@decsai.ugr.es\">Maria J. Martin-Bautista<\/a><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Session Code<\/p>\n<p>Please use the following code when submitting your paper to this session: <strong title=\"Special Session Code: SS2-LLMS\">SS2-LLMS<\/strong><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Session description<\/p>\n<p>This session focuses on the transformative role of large language models (LLMs) in enabling advanced analysis and generation of data from the web and social media. It aims to bring together researchers, practitioners, and industry experts interested in how LLMs are reshaping our ability to interpret, summarize, retrieve, and generate content across large-scale, unstructured, and dynamic data environments.<\/p>\n<p>Contributions are welcome from both theoretical and applied perspectives, covering novel methodologies, system architectures, and real-world applications. The session also encourages submissions that address critical issues related to transparency, ethics, and responsible use of LLMs in open and high-impact domains.<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Topics of Interest<\/p>\n<ul>\n<li><strong>LLMs for Social Media Analysis.<\/strong> Investigating the use of LLMs to extract insights from social media platforms, including sentiment and stance detection, topic modelling, misinformation identification, and audience analysis.<\/li>\n<li><strong>Text Understanding and Knowledge Extraction with LLMs.<\/strong> Approaches leveraging LLMs for summarization, classification, information extraction, question answering, and retrieval-augmented generation (RAG) over complex and noisy text sources.<\/li>\n<li><strong>Multimodal and Hybrid LLM Applications.<\/strong> Integration of LLMs with other data modalities (e.g., visual, audio, structured data) or symbolic systems to enhance performance in cross-domain analysis and interactive AI systems.<\/li>\n<li><strong>Real-Time and Context-Aware Processing.<\/strong> Using LLMs for streaming data analysis, adaptive content generation, and real-time decision support, particularly in fast-changing web and social media environments.<\/li>\n<li><strong>Ethics, Safety, and Responsible Deployment of LLMs.<\/strong> Exploring issues of fairness, bias, explainability, and user privacy in LLM-based applications, with emphasis on accountability and trustworthy AI in high-impact contexts.<\/li>\n<\/ul>\n<p style=\"font-size: 1.2em;font-weight: bold\">Target Audience<\/p>\n<p>This session is intended for researchers, practitioners, data scientists, and industry experts working in LLMs, generative AI, and web\/data analysis, with a particular focus on applications to social media and text data analysis. It also welcomes professionals interested in the ethical, methodological, and practical implications of applying LLMs and generative AI to data-driven decision-making.<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.4em;font-weight: bold\" id=\"ss3\"><em title=\"Special Session Code: SS3-SPACE\">SS3-SPACE:<\/em> AI for Space Exploration<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Proposers<\/p>\n<p><a href=\"mailto:ignacio@nasa.gov\">Ignacio G. L\u00f3pez-Francos<\/a>, <a href=\"mailto:andres.martinez@nasa.gov\">Andr\u00e9s Martinez<\/a> and <a href=\"mailto:fedeloz@uma.es\">Federico Lozano-Cuadra<\/a><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Session Code<\/p>\n<p>Please use the following code when submitting your paper to this session: <strong title=\"Special Session Code: SS3-SPACE\">SS3-SPACE<\/strong><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Abstract<\/p>\n<p>Artificial Intelligence has long played a role in space exploration. NASA has developed and flown AI technologies for over four decades, from early demonstrations in automated planning and scheduling, to Earth observation systems capable of mapping and responding to geographical events from orbit, to autonomous landers and rovers conducting science on Mars. Today, space exploration stands at a critical inflection point where advances in AI (particularly with the recent generative AI wave) have the potential to dramatically expand scientific discovery and mission operational capability.<\/p>\n<p>Several converging drivers underscore the timeliness of this topic. The cost of launch continues to decline, enabling a surge of high-capitalized commercial entrants and accelerating national programs such as NASA\u2019s Artemis campaign, which aims to return humans to the Moon and prepare for Mars exploration. Moreover, NASA JPL\u2019s upcoming CADRE (Cooperative Autonomous Distributed Robotic Exploration) mission will deploy a swarm of rovers on the lunar surface, demonstrating how multi-agent autonomy is becoming central to future space missions. At the same time, intensifying geopolitical competition has catalyzed a renewed space race, reminiscent of the Cold War, but now occurring in a far more complex technological ecosystem. These developments demand unprecedented levels of autonomy, adaptability, and decision support \u2014 areas where AI has become indispensable.<\/p>\n<p>This Special Session will address the technical challenges and opportunities of deploying advanced AI in space systems. Key issues include:<\/p>\n<ul>\n<li><strong>Scalability of science and mission operations:<\/strong> Leveraging AI to analyze massive volumes of multisensor data collected from spacecraft, orbiters, and planetary missions.<\/li>\n<li><strong>Autonomous exploration:<\/strong> Developing AI-driven agents capable of \u201csingle-shot\u201d or minimally supervised missions to remote worlds where real-time ground control is infeasible.<\/li>\n<li><strong>Validation, verification, and certification:<\/strong> Ensuring trust, safety, and robustness of AI algorithms when human lives and mission-critical objectives are at stake.<\/li>\n<li><strong>Generalization to novel environments:<\/strong> Designing algorithms that can adapt to out-of-distribution conditions characteristic of extraterrestrial terrains and atmospheres.<\/li>\n<li><strong>Human-AI teaming:<\/strong> Enabling effective collaboration between astronauts, mission controllers, and AI systems in dynamic and uncertain operational contexts.<\/li>\n<\/ul>\n<p>The timeliness of this session stems from the convergence of two forces: the maturation of AI into powerful foundation technologies, and the emergence of a new space economy driven by both government agencies and private industry. The synergy between these trends makes AI not only relevant but essential for realizing ambitious goals such as sustained lunar presence, Mars exploration, and long-duration autonomous missions in deep space.<\/p>\n<p>By bringing together experts in AI, robotics, aerospace engineering, and planetary science, this Special Session will provide a focused forum to assess progress, highlight open challenges, and shape the research agenda for the coming decade. In doing so, it will underscore how breakthroughs in AI can unlock new frontiers of human and robotic exploration beyond Earth.<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.4em;font-weight: bold\" id=\"ss4\"><em title=\"Special Session Code: SS4-FINANCE\">SS4-FINANCE:<\/em> Foundation and Emerging AI Models for Applications in Finance, Cyber Security, and Life Sciences<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Proposers<\/p>\n<p><a href=\"mailto:agapito@unicz.it\">Giuseppe Agapito<\/a>, <a href=\"mailto:fcauteruccio@unisa.it\">Francesco Cauteruccio<\/a>, <a href=\"mailto:e.corradini@univpm.it\">Enrico Corradini<\/a> and <a href=\"mailto:andrea.tundis@bundesbank.de\">Andrea Tundis<\/a><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Session Code<\/p>\n<p>Please use the following code when submitting your paper to this session: <strong title=\"Special Session Code: SS4-FINANCE\">SS4-FINANCE<\/strong><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Abstract<\/p>\n<p>Artificial Intelligence (AI) is reshaping modern societies, driving innovation in finance, cybersecurity, logistics, and life sciences. With the rise of foundation models and emerging AI architectures, there is both opportunity and urgency to explore how these methods can ensure resilience, trust, and efficiency in critical infrastructures. This special session brings together researchers from diverse domains to discuss methodologies and applications that leverage AI for robustness, security, and sustainability.<\/p>\n<p>The growing complexity of digital ecosystems raises common challenges: how to design infrastructures that remain secure, adaptive, and resilient under uncertainty. In finance, the emergence of digital and central bank digital currencies (CBDCs) demands intelligent mechanisms for stability and systemic risk protection. Similar concerns arise in cybersecurity and networked systems, where AI-driven analysis of complex networks and IoT security are key to anticipating threats and ensuring robustness. In life sciences, large-scale biomedical data, genomics, and proteomics require advanced AI and graph mining methods, supported by high-performance and energy-aware computing to enable scalable, sustainable healthcare analytics.<\/p>\n<p>Bridging these domains highlights a shared reliance on AI to analyze and manage complexity\u2014from spatiotemporal graph transformers for logistics, to algorithms for resilience in digital infrastructures, to biomedical network mining. The integration of network science, intelligent systems, and AI optimization underscores both the interdisciplinary nature of today\u2019s challenges and the opportunities for impactful solutions. This special session timely addresses these converging needs, advancing AI models and applications for critical infrastructures.<\/p>\n<p>Topics of interest include, but not limited to:<\/p>\n<ul>\n<li>Intelligent systems<\/li>\n<li>Resilience in Critical Infrastructures<\/li>\n<li>Artificial Intelligence<\/li>\n<li>Payment systems<\/li>\n<li>Digital Currency<\/li>\n<li>CBDC systems<\/li>\n<li>Spatiotemporal Graph Transformers<\/li>\n<li>Last-Mile Delivery Optimization<\/li>\n<li>Demand Forecasting &amp; OD Estimation<\/li>\n<li>Resilience &amp; Robustness of Logistics Networks<\/li>\n<li>Digital Twins for Urban Logistics<\/li>\n<li>Energy-\/Emission-Aware Routing<\/li>\n<li>Network Science and Analysis<\/li>\n<li>Complex Networks<\/li>\n<li>Internet of Things<\/li>\n<li>Algorithms and Games<\/li>\n<li>Data Science<\/li>\n<li>Cybersecurity<\/li>\n<li>Data Mining for Biomedical Big Data<\/li>\n<li>Genomics and Proteomics Analytics<\/li>\n<li>Graph Mining in Life Sciences<\/li>\n<li>Parallel and Distributed Algorithms for Healthcare<\/li>\n<li>Machine Learning and AI for Health<\/li>\n<li>Energy-Aware Computing in Healthcare HPC<\/li>\n<li>High-Performance Computing (HPC) in Life Sciences<\/li>\n<li>Biological Network Analysis<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<hr \/>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.4em;font-weight: bold\" id=\"ss5\"><em title=\"Special Session Code: SS5-ETHIC\">SS5-ETHIC:<\/em> From the Streets to the Systems: A Journey Through Ethical AI, Workforce Justice, and Radical Inclusion<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Proposers<\/p>\n<p><a href=\"mailto:chope@thelooplab.org\">Chris Hope<\/a> and <a href=\"mailto:jvalls@ugr.es\">Javier Valls Prieto<\/a><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Session Code<\/p>\n<p>Please use the following code when submitting your paper to this session: <strong title=\"Special Session Code: SS5-ETHIC\">SS5-ETHIC<\/strong><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Abstract<\/p>\n<p>Artificial Intelligence is reshaping economies, governance, and culture, but the communities<br \/>\nmost affected by inequity are often the least prepared to navigate this transformation. From<br \/>\nRoma populations in Europe to Black and Latino learners in the United States, systemic barriers<br \/>\nin education, digital access, and workforce opportunity continue to widen the AI literacy gap.<\/p>\n<p>This special session brings together two perspectives grounded in lived experience and legal<br \/>\nscholarship to propose a transatlantic framework for ethical AI inclusion.<\/p>\n<p>Together, we will present three outcomes:<\/p>\n<ol>\n<li>Fairness metrics to measure and reduce algorithmic bias.<\/li>\n<li>Culturally grounded, bilingual curricula that expand digital capacity.<\/li>\n<li>Policy and legal levers bridging U.S. and EU protections.<\/li>\n<\/ol>\n<p>This session models radical inclusion while challenging IEEE stakeholders to move beyond bias<br \/>\nawareness toward measurable equity and structural justice.<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.4em;font-weight: bold\" id=\"ss6\"><em title=\"Special Session Code: SS6-FIRE\">SS6-FIRE:<\/em> FIRE-AI: Federated Intelligence for Responsible, Evolving AI<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Proposers<\/p>\n<p><a href=\"mailto:dipanwita.thakur@unical.it\">Dr. Dipanwita Thakur<\/a>, <a href=\"mailto:giancarlo.fortino@unical.it\">Prof. Giancarlo Fortino<\/a>, <a href=\"mailto:sdas@mst.edu\">Prof. Sajal K. Das<\/a> and <a href=\"mailto:torsten.braun@inf.unibe.ch\">Prof. Dr. Torsten Braun<\/a><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Session Code<\/p>\n<p>Please use the following code when submitting your paper to this session: <strong title=\"Special Session Code: SS6-FIRE\">SS6-FIRE<\/strong><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Technical Description<\/p>\n<p>Federated Learning (FL) has become a foundational approach to privacy-preserving, distributed machine learning across diverse domains such as healthcare, finance, IoT, and edge computing. However, classical FL methods\u2014largely focused on optimization and scalability\u2014face growing limitations as artificial intelligence enters a new era driven by large foundation models (e.g., LLMs and multi-modal architectures). The next generation of federated systems must move beyond efficiency alone, embracing responsibility, autonomy, personalization, and ethical alignment with societal needs.<\/p>\n<p>The FIRE-AI Special Session addresses these challenges by exploring the emerging concept of federated intelligence: distributed AI systems that are not only technically robust but also socially aware, ethically grounded, and capable of adapting foundation models in decentralized environments.<\/p>\n<p>Key issues include:<\/p>\n<ul>\n<li><strong>Federated fine-tuning of foundation models:<\/strong> techniques for personalization, efficiency, and privacy-aware adaptation.<\/li>\n<li><strong>Decentralized architectures:<\/strong> robust, agent-based, and zero-trust systems for scalable federated intelligence.<\/li>\n<li><strong>Responsible and ethical FL:<\/strong> embedding fairness, accountability, regulatory compliance, and sustainability by design.<\/li>\n<li><strong>Cross-domain applications:<\/strong> federated AI for healthcare, finance, smart environments, and IoT.<\/li>\n<li><strong>Green and efficient FL:<\/strong> optimization techniques for reducing energy footprint and communication overhead.<\/li>\n<\/ul>\n<p>The timeliness of this session stems from two converging trends: (1) the deployment of foundation models across sectors where privacy and distributed data are critical, and (2) the growing societal demand for AI that is trustworthy, transparent, and sustainable. Recent workshops (e.g., FLBD2024, FL@FM-NeurIPS 2024, CFAgentic@ICML 2025, FLUID@AAAI 2025) have laid groundwork in federated optimization and privacy, but few have explicitly tackled the intersection of FL, foundation models, and responsible AI. This Special Session aims to fill that gap by fostering a dialogue between federated learning researchers, distributed systems experts, and the responsible AI community.<\/p>\n<p>Through this session, we expect to create a multidisciplinary forum that advances the theory and practice of federated intelligence, identifies open problems at the intersection of technical and ethical dimensions, and outlines a roadmap for future research and deployment.<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.4em;font-weight: bold\" id=\"ss7\"><em title=\"Special Session Code: SS7-DRONES\">SS7-DRONES:<\/em> AI-Enabled Drones for Environmental Monitoring and Sustainable Land Management<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Proposers<\/p>\n<p><a href=\"mailto:jesusrc@ugr.es\">Jes\u00fas Rodrigo-Comino<\/a>, Christos Xouris, Manuel Seeger and Athanasios Kalogeras<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Session Code<\/p>\n<p>Please use the following code when submitting your paper to this session: <strong title=\"Special Session Code: SS7-DRONES\">SS7-DRONES<\/strong><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Technical description, scope, and timeliness<\/p>\n<p>Unmanned Aerial Vehicles (UAVs) have become essential platforms for quantifying environmental change at actionable spatial and temporal scales. In parallel, advances in computer vision, lightweight transformers, and edge accelerators now make it feasible to run robust AI models on-board, closing the loop from data capture to real-time decision support. This Special Session will convene researchers and practitioners working at the intersection of drones, AI, and sustainability to present methods and field deployments that deliver measurable environmental impact.<\/p>\n<p>The session will emphasize seven technical themes:<\/p>\n<ol>\n<li>Multimodal perception and fusion across RGB, multispectral, thermal, hyperspectral, and LiDAR.<\/li>\n<li>Edge learning, pruning\/distillation, and compression for low-power inference.<\/li>\n<li>Domain adaptation, transfer learning, and generalization across sites, seasons, and sensors.<\/li>\n<li>Uncertainty quantification, calibration, and explainability (XAI) to build usertrust.<\/li>\n<li>Autonomous navigation, coverage path planning, and energy-aware swarms.<\/li>\n<li>Privacy-preserving and federated learning with secure data governance.<\/li>\n<li>Benchmarks, open datasets, and reproducible evaluation protocols.<\/li>\n<\/ol>\n<p>Target application domains include post-disaster assessment (e.g., floods and DANAs), geomorphological change detection and soil erosion, wildfire fuel mapping and moisture estimation, precision agriculture and water-stress monitoring\/droughts, biodiversity and habitat surveys, coastal dynamics, and urban micro-climate\/heat-island analysis. The session aligns with the CAI 2026 vertical \u201cSustainable and Trustworthy AI\u201d by prioritizing methods that reduce resource consumption, quantify model risk, and support transparent, safe, and human-centered decision making.<\/p>\n<p>This topic is timely due to rapidly falling costs of UAVs and sensors, the emergence of edge-ready vision foundation models, and urgent policy drivers such as the EU Green Deal and national climate adaptation strategies. By bringing together AI, remote sensing, geomatics, and public administration, the session aims to accelerate the transition from prototypes to deployable tools, foster cross-site generalization, and define best practices for trustworthy AI-enabled environmental monitoring.<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.4em;font-weight: bold\" id=\"ss8\"><em title=\"Special Session Code: SS8-INDUSTRY\">SS8-INDUSTRY:<\/em> Industry and AI<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Proposers<\/p>\n<p><a href=\"mailto:jesus@decsai.ugr.es\">Jes\u00fas Chamorro<\/a> and <a href=\"mailto:ming.hou@ieee.org\">Ming Hou<\/a><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Session Code<\/p>\n<p>Please use the following code when submitting your paper to this session: <strong title=\"Special Session Code: SS8-INDUSTRY\">SS8-INDUSTRY<\/strong><\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Abstract<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The following Special Sessions will be carried out at CAI 2026: SS1-MULTI: (Multi-)Agentic AI in Action: Real-World Applications of Contemporary Multi-Agent Systems SS2-LLMS: Emerging Trends in Data, Web, and Social Media Analysis and Generation with LLMs SS3-SPACE: AI for Space Exploration SS4-FINANCE: Foundation and Emerging AI Models for Applications in Finance, Cyber Security, and Life&#8230;<\/p>\n","protected":false},"author":2627,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-215","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - 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