This paper presents a novel approach for tracking deformable contour targets using Unmanned Aerial Vehicles (UAVs). The proposed scheme combines image moments descriptor and Event-Triggered (ET) Nonlinear Model Predictive Control (NMPC) for efficient and accurate tracking. The deformable contour model allows adaptation to the evolving target's shape, while the proposed event-triggered scheme achieves improved computational efficiency and extended flight duration while generating new control sequences for the UAV. Real-world experimental validation as well as a comparative simulation performance analysis validate the scheme, showcasing its robustness in handling complex scenarios. This approach holds promise for various applications, such as surveillance and autonomous navigation.

A hybrid model-based and data-driven framework is proposed in this paper for autonomous coastline surveillance using an unmanned aerial vehicle. The proposed approach comprises three individual neural network-assisted modules that work together to estimate the state of the target (i.e., shoreline) to contribute to its identification and tracking. The shoreline is first detected through image segmentation using a Convolutional Neural Network. The part of the segmented image that includes the detected shoreline is then fed into a CNN real-time optical flow estimator. The position of pixels belonging to the detected shoreline, as well as the initial approximation of the shoreline motion, are incorporated into a neural network-aided Extended Kalman Filter that learns from data and can provide on-line motion estimation of the shoreline (i.e., position and velocity in the presence of waves) using the system and measurement models with partial knowledge. Finally, the estimated feedback is provided to a Partitioned Visual Servo tracking controller for autonomous multirotor navigation along the coast, ensuring that the latter will always remain inside the onboard camera field of view. A series of outdoor comparative studies using an octocopter flying along the shoreline in various weather and beach settings demonstrate the effectiveness of the suggested architecture.

This paper addresses the problem of water sampling by using a multirotor UAV with a cable-suspended mechanism. In order to ensure the safe execution of the sampling procedure and the stabilization of the vehicle, the disturbances, induced by the water flow and transferred through the cable, have to be identified. Specifically, an estimate of the disturbances is extracted by integrating a depth sensor, a load cell, an ultrasonic sensor and a downward-looking camera into the UAV's sensor suite and fusing the respective measurements. Gaussian Processes are afterwards employed so as to learn the uncertain disturbances in real time and in a non-parametric manner. The predicted disturbances are incorporated into a geometric control scheme which is capable of stabilizing the UAV above the desired sampling position while compensating for the aforementioned disturbances. The performance of the proposed control strategy is demonstrated through both simulation and experimental results.

The concept of Data Spaces has gained traction in recent years. Major representatives emerged which have the technological maturity as well as support by relevant decision and policy makers (e.g. the International Data Spaces Association (IDSA) or Gaia-X). These follow different architectural approaches. In this session we want to illustrate the challenges of integrating the Data Space architectures with established concepts of Spatial Data Infrastructure.During the next 4 years, the ENFORCE project (Empower citizeNs to join Forces with public authORities in proteCting the Environment) is dedicated to fostering sustainable practices and ensuring environmental regulatory compliance by integrating citizen science with innovative technologies. By employing Living Labs and citizen science methodologies, ENFORCE will create innovative tools that bridge the gap between data reporting, monitoring, and policy enforcement. The project integrates data collection (e.g. Copernicus satellite data), analysis, and stakeholder participation to meet these goals. ENFORCE will leverage geospatial intelligence and explainable AI to enhance environmental governance. These tools and strategies will be tested and refined at eight pilot sites in seven countries, supplemented by capacity-building and policy recommendation efforts.The design and development of a geospatial information infrastructure that supports the envisioned data workflows is a key challenge addressed by ENFORCE. This infrastructure will prioritize the integration of OGC API-driven systems into the Data Space ecosystem, forming a central component of the project’s agenda. Through development of a blueprint architecture for integration, the project will identify gaps and missing components in current systems, aligning with standards such as the FAIR principles and open data. The concepts will be facilitated in an ENFORCE “Tools Plaza”, an innovative platform providing data science and analytical capabilities for environmental compliance workflows.

Europe faces a growing frequency of extreme weather events, from heatwaves and floods to wildfires and earthquakes, increasingly threatening urban environments. Unusually warm winters are becoming progressively common, destabilizing ecosystems and altering traditional weather dynamics. Addressing these crucial changes, the CLIMRES project aims to foster a “Leadership for Climate-Resilient Buildings” by identifying and categorizing vulnerabilities within the built environment and assessing their effects within urban systems. This effort integrates diverse data sources, including Copernicus services, IoT networks, and municipal datasets, and considers hazard warnings and weather forecasts. Moreover, a liaison with the Destination Earth initiative enhances the project with the capacity to leverage extreme weather predictions and future climate models. CLIMRES aims to deliver vulnerability assessment and impact evaluation methodologies, along with a “hub of measures” inventory for cost-effective building design and materials against climate risks, as well as decision support tools, to aid building owners, policymakers and stakeholders in planning effective interventions and to address vulnerabilities, targeting three levels of decision making at strategic, tactical and operational levels. The project deploys cloud technologies like OpenStack and Kubernetes to host an interoperable platform for vulnerability analysis, data harmonization, and decision-making. Its solutions will be tested and validated on 3 Large Scale Pilots in Spain, Greece, Italy, and Slovenia, addressing hazards such as heatwaves, flooding, fires, and earthquakes. A multi-hazard replication pilot in France will further evaluate the scalability and versatility of these approaches across diverse contexts. Insights from these pilots will feed into a replication roadmap and a capacity-building program designed to train future leaders in climate-resilient urban development. By fostering co-creation with local stakeholders and communities, CLIMRES ensures its innovative solutions are practical, cost-effective, and replicable, targeting Technology Readiness Levels (TRL) 6-8. CLIMRES aims to bridge innovation with actionable solutions, equipping building owners, policymakers, and communities with the tools needed to enhance urban climate resilience. This presentation highlights the project’s interdisciplinary approach, outputs and technological underpinnings, offering insights into scalable solutions for climate adaptation in urban settings.