Marica Antonacci

CLIMRES (LEADERSHIP FOR CLIMATE RESILIENT BUILDINGS) is a European project addressing the growing vulnerability of buildings and urban environments to climate change impacts. The project combines climate data, Earth Observation (EO) products, and impact assessment methodologies to identify climate-driven hazards, assess building and urban-scale vulnerabilities, and support decision-making through dedicated tools and measures. These solutions are validated through large-scale pilot demonstrations across several European countries. A key enabling outcome of CLIMRES is the design and deployment of a scalable, cloud-native infrastructure hosting the project’s Federated Data Exchange Platform (FDXP) and digital services. The infrastructure is hosted by ECMWF on a dedicated Kubernetes cluster within the Common Cloud Infrastructure (CCI), part of the European Weather Cloud co-managed by ECMWF and EUMETSAT. The underlying cloud environment is based on OpenStack with Ceph storage, providing elastic compute and scalable object storage capabilities for data-intensive workloads. This infrastructure provides the technical backbone for integrating heterogeneous datasets, executing data-processing workflows, and delivering operational services that underpin climate resilience assessments and decision-support applications. The CLIMRES platform follows cloud-native design principles and adopts containerization and microservice-based architectures to ensure modularity, scalability, and operational robustness. Kubernetes is used as the core orchestration layer, while Rancher provides centralized cluster management, monitoring, and operational visibility. All services, including the FDXP and supporting applications, are deployed consistently across environments using GitOps principles, ensuring reproducibility, traceability, and elimination of configuration drift. Continuous Integration and Continuous Delivery (CI/CD) pipelines automate the full software lifecycle, from source code changes to container image building and deployment. Docker images are built through automated pipelines and deployed via Git-driven workflows, enabling transparent, auditable, and predictable releases. Semantic versioning and changelog generation are fully automated, ensuring consistent release management across services. This contribution presents the CLIMRES cloud infrastructure as a production-ready case study for EO- and climate-driven applications. It demonstrates how cloud-native technologies can effectively support scalable data management platforms and operational services for climate resilience.

MediTwin is a European research initiative aimed at developing digital twin technologies for the Mediterranean region, integrating Earth observation data, numerical modelling and artificial intelligence to support environmental monitoring and decision-making. In this context, the MediTwin Summer School 2025 was organised to provide hands-on training on data-driven workflows, cloud-native tools and AI/ML techniques for Earth system applications. The school targeted early-career researchers, PhD students and technical staff from research institutions, with a total of 20 participants. The School required a scalable, secure and reproducible cloud infrastructure capable of supporting hands-on training activities in Earth system digital twins, data analysis and AI/ML workflows. This contribution presents the design and provisioning of the cloud-native infrastructure deployed to support the school, with a focus on Infrastructure as Code (IaC), Kubernetes-based orchestration and hybrid GPU-enabled workloads. The infrastructure was deployed on the ECMWF on-premises cloud, based on OpenStack and backed by Ceph software-defined storage, providing elastic compute, networking and persistent storage services. The Kubernetes cluster was provisioned in a high-availability configuration using Terraform and Rancher Cluster Manager, following established GitOps best practices. The cluster architecture comprised dedicated control-plane, worker, ingress and GPU nodes, enabling both standard cloud-native services and accelerated AI/ML workloads. Cluster lifecycle management, configuration drift prevention and application delivery were handled through a GitOps approach using Rancher Fleet. GitLab acted as the central orchestration platform for source control, CI/CD pipelines and IaC automation, hosting Terraform modules, Helm charts, Rancher cluster definitions and configuration templates. This ensured full traceability, auditability and reproducibility of both infrastructure and application deployments. Sensitive credentials and API keys were securely managed using HashiCorp Vault and dynamically injected into workloads. To support interactive training activities, a JupyterHub service was deployed on Kubernetes using the official Helm chart, customised for resource management, authentication and storage integration. GPU acceleration was enabled via the NVIDIA GPU Operator, which automated driver installation, device discovery and scheduler integration. In addition, outside the Kubernetes environment, 20 GPU-enabled virtual machines were provisioned directly on OpenStack using an Ansible role executed through AWX, itself deployed on the Kubernetes cluster, to accommodate specific student exercises requiring isolated VM-based access. This experience demonstrates how modern cloud-native and DevSecOps practices can be effectively applied to provision short-lived yet production-grade scientific training infrastructures, ensuring scalability, security and reproducibility for future Earth observation and digital twin education initiatives.

Large-scale foundation models trained on multi-sensor satellite imagery has been driving recent advances in Earth Observation (EO) tasks. Although such models achieve impressive transferability across diverse downstream tasks, their computational and memory demands hinder accessibility, reproducibility, and deployment in resource-constrained environments. This work explores a compact and efficient alternative, introducing a metadata-aware Mixture-of-Experts Masked Autoencoder (MoE-MAE) for EO representation learning (Albughdadi, 2025).The proposed MoE-MAE is a self-supervised transformer-based architecture with only 2.5 million parameters. It combines sparse expert routing and geo-temporal conditioning. The sparse routing allows token specialization while keeping active computation low. The geo-temporal conditioning injects information about latitude, longitude, and cyclic temporal attributes directly into the model. The proposed design enables the algorithm to exploit spatial and temporal regularities inherent in EO data without requiring dense, and computationally costly transformers.The model is pretrained in the BigEarthNet-Landsat (BEN-LS) (Corley et al., 2025) dataset using a masked reconstruction loss function augmented with auxiliary unmasked and load-balancing losses to encourage stable expert utilization. The learned encoder representations are then evaluated using linear probing on two benchmark datasets: (1) BEN-LS, a multi-label land-cover dataset with explicit metadata, and (2) EuroSAT-Landsat (EuroSAT-LS) (Corley et al., 2025), a single-label classification datasets without metadata. Despite the encoder’s small size (~2.3 M parameters), the proposed MoE-MAE achieves competitive results with models’ orders of magnitude larger. On BEN-LS, the frozen encoder achieves a micro mean average precision of 0.767, comparable to SSL4EO-L ViT-S/16 MoCo v2 (0.775) (Stewart et al., 2023). On EuroSAT-LS, the model maintains strong transferability, achieving 84.2% accuracy, even in the absence of geo-temporal metadata.Expert specialization across spatial patterns is revealed through adequate ablation and visualization studies, which show that some experts respond primarily to vegetation, others to water or textured regions. This demonstrates interpretable behaviour and complementary feature learning. Additionally, only about half of the model’s expert feed-forward capacity is activated per token, confirming computational sparsity in practice. These findings suggests that such models can retain strong representational power while substantially reducing training and inference costs.This work presents a first step toward small-scale architectures for EO representation learning by integrating metadata, and leveraging sparse computation to approach the performance of massive transformers. Future work will extend this framework to multi-sensor and multi-temporal datasets to capture dynamic Earth processes efficiently.Albughdadi, M. (2025). Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation.arXiv:2509.10919.Stewart, A. J., Lehmann, N., Corley, I. A., Wang, Y., Chang, Y.-C., Braham, N. A. A., Sehgal, S., Robinson, C., & Banerjee, A. (2023). SSL4EO-L: Datasets and Foundation Models for Landsat Imagery. arXiv:2312.05241.Corley, I., Sharma, L., and Crasto, R. (2025). Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models.arXiv:2506.08780.

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.

The detection of environmental changes caused by natural disasters is critical for rapid response and effective management. In this study, we present a methodology for unsupervised change detection that leverges optical Sentinel-2 [1] and Synthetic Aperture Radar (SAR) Sentinel-1 [2] accessed through public SpatioTemporal Asset Catalogs (STAC) [3] along with Earth Observation (EO) foundation model, namely, Clay [4]. The analysis was conducted independently for each dataset to capitalize on the unique properties of these satellite sensors. Sentinel-1 offers robust surface texture sensitivity with its all-weather, day-and-night imaging capability, while Sentinel-2 provides detailed spectral and spatial information critical for vegetation and land-use analysis.Clay foundation model, a large-scale pretrained Vision Transformer trained on EO data from various missions (Sentinel-1, Sentinel-2, Landsat, Planet, NAIP, LINZ, and MODIS), was used to extract spatially and spectrally rich embedding from Sentinel-1 and Sentinel-2 images. The model takes as an input the satellite imagery along with information about location and time and outputs mathematical representations of a given area at a certain time on Earth’s surface. The images were fed to the model as patches of size 256×256 along with the timestap of the scene, the spatial location and other metadata of the input image to estimate the embeddings that can be rearranged to be of size (1024×32×32). These embeddings were then analyzed using pixel-wise distance metrics to quantify changes between pre- and post-even imagery and the resulting distance image was then spatially interpolated to the size of the input image.The approach was validated on satellite imagery of the Valencia region in Spain, an area significantly impacted by recent flooding on the 29th October 2024. For Sentinel-1, the method effectively highlighted surface water changes and structure affected by the floods in two scenes acquired on the 7th October and the 12th November 2024, while Sentinel-2 data captured variations in vegetation areas that was impacted by the floods using two scenes acquired on the 1st October and the 10 November 2024. By analyzing the datasets independently, this framework demonstrates the complementary insights offered by radar and optical imagery in assessing disaster impacts.This study highlights the potential of leveraging open satellite data available via STAC catalogs and EO foundation models for unsupervised change detection in disaster monitoring, enabling rapid response without relying on specialized models tailored to specific regions. Unlike traditional approaches that require retraining for new areas due to geographical variability, this methodology is both scalable and adaptable, providing a generalizable framework for environmental monitoring, disaster response, and resilience planning. The results emphasize the value of integrating multi-sensor satellite imagery to enhance understanding of disaster impacts, facilitating more informed and timely decision-making.References:[1] https://earth-search.aws.element84.com/v1[2] https://planetarycomputer.microsoft.com/api/stac/v1[3] https://stacspec.org/en[4] https://clay-foundation.github.io/model/index.html

The Copernicus program has fostered Earth Observation (EO) and Earth Modeling by offering extensive data and services to European Citizens. Sentinel satellites’ data is accessible through platforms like the Copernicus Open Access Hub and the Copernicus Data Space Ecosystem, which provide a wide range of information on land, ocean and atmospheric conditions. Complementing these resources, six specialized Copernicus services deliver data in domains such as the atmosphere, marine environment, land monitoring, climate change, security and emergency response. To streamline access and usability, cloud-based Copernicus Data and Information Access Services (DIAS) offer centralised platforms equipped with cloud infrastructure and processing tools. Building on these efforts, the Copernicus Data Space Ecosystem (https://dataspace.copernicus.eu/) enhances existing DIAS services with advanced functionalities like improved search capabilities, virtualizations and APIs. Meanwhile, the Destination Earth (DestinE) initiative led by ECMWF, EUMETSAT and ESA, aims to develop high-precision digital Earth models - or digital twins - that simulate natural and human activities. These models mainly focus on weather-induced extremes and climate change adaptation, generating valuable Earth Modeling data. Furthermore, European Data Spaces integrate datasets across diverse domains, including agriculture, health, energy, and environmental monitoring, creating opportunities to combine these resources with Copernicus and DestinE data through advanced technologies like artificial intelligence (AI) and machine learning (ML). This integration paves the way for innovative solutions and public-facing products and services. Despite the volume and richness of Copernicus and related EO data, its accessibility remains limited, with most users being experts or scientists. For broader industry adoption and the development of impactful applications that benefit society and the enviroment, significant barriers must be addressed. EO data is often fragmented, complex, and difficult to process, requiring domain expertise for tasks such as data discovery, pre-processing, storage, and conversion into formats suitable for analytics and Geographic Information Systems (GIS). The EO4EU platform (https://www.eo4eu.eu/), showcased in this presentation, introduces a multi-cloud ecosystem designed for holistic management of EO data. Its primary objective is to bridge the gap between domain experts and end users, leveraging technological advancements to broaden the adoption of EO data across diverse markets. By enhancing the accessibility and usability of EO data, EO4EU supports market growth through advanced data modeling, dynamic annotation, and state-of-the-art processing, powered by European cloud infrastructures such as WEkEO/DIAS and CINECA. EO4EU provides a suite of innovative tools and methodologies to assist a wide range of users, from professionals and domain experts to general citizens, in benefiting from EO data. Its key features include: Knowledge Graph-based Decision Making: Facilitates insightful feature extraction from diverse repositories, enabling a more comprehensive understanding of datasets. AI/ML Marketplace: A centralized hub for AI & ML models, algorithms, techniques, and metadata. Big Data Processing Engines: Optimized for cloud environments to efficiently manage large-scale datasets. User-friendly Interfaces: GUI, CLI, APIs, and immersive VR experiences, targeting both technical and non-technical users. Workflow Engine: Simplifies the definition and execution of recurring tasks for EO data retrieval and processing.

The BUILDSPACE project harnesses the transformative potential of cloud computing to evolve urban development and resilience practices. By integrating advanced Earth Observation (EO) data with state-of-the-art satellite and cloud technologies, BUILDSPACE addresses critical urban challenges, including climate adaptation, energy efficiency, and disaster resilience, while contributing to the European Green Deal’s objectives of sustainability and carbon neutrality. Central to BUILDSPACE are five innovative services designed to support urban decision-making. At the building scale, the project facilitates the generation and visualization of detailed digital twins through interactive displays, virtual reality (VR), and augmented reality (AR) interfaces. These digital twins enable precise simulations for energy optimization, operational efficiency, and climate impact assessment. At the city scale, BUILDSPACE provides tools to address climate scenarios, such as urban heat islands and flooding, empowering municipalities and urban planners with actionable insights through interactive, map-based platforms. The project’s technical foundation lies in a robust, cloud-native architecture built on Kubernetes and OpenStack, combined with a DevOps methodology to streamline both infrastructure services and application deployment. Kubernetes orchestrates containerised workloads, enabling efficient automated deployment, scaling and management of applications, while OpenStack provides a flexible infrastructure for managing compute, storage, and networking resources. Through the DevOps approach, we ensure continuous integration and delivery (CI/CD), fostering rapid development cycles and operational agility. By adopting open-source cloud platforms, the project ensures interoperability, reproducibility and automation across diverse environments, driving consistency and efficiency throughout the lifecycle of both infrastructure and applications. The project’s services are being validated across four European cities representing diverse climatic conditions, namely Warsaw, Riga, Piraeus and Ljubljana. These validations focus on two scenarios: construction companies monitoring building processes with advanced digital tools, and municipalities analysing the impacts of climate change on urban infrastructure. By advancing from TRL 5-6 to TRL 7-8, BUILDSPACE aims to deliver market-ready solutions that align with the European GNSS and Copernicus initiatives and to synchronise with the advances, concerning Digital Twin technologies and data federation mechanisms, of the Destination Earth initiative, while paving the way for a broader adoption of cloud technologies in EO-based urban resilience applications.

In the dynamic landscape of digital security, safeguarding information assets stands as a paramount concern for organizations. This paper presents a comprehensive initiative undertaken by INFN, a prominent player in research, to bolster the security posture of its open-source components within the DataCloud production middleware. Central to this initiative is the recognition of the pivotal role security plays in the software development lifecycle (SDLC). The paper outlines INFN’s strategic approach to align with industry standards such as OWASP SAMM and ISO/IEC 27002 frameworks. Through collaboration and proactive measures, INFN aims to establish virtuous processes aimed at enhancing security governance, self-assessment, continuous monitoring, and timely responses to emerging vulnerabilities. The ultimate goal is to cultivate a more secure and resilient software ecosystem tailored to scientific data analysis.

The National Institute for Nuclear Physics (INFN) has been operating and supporting Italy’s largest research and academic distributed infrastructure for several decades. In March 2021, INFN launched the “INFN Cloud” initiative which provides a federated cloud infrastructure and a customizable service portfolio tailored for the scientific communities supported by the institute. The federation middleware of the INFN Cloud platform is built upon the INDIGO PaaS Orchestration system, which consists of interconnected open-source microservices. Among them, there is the INDIGO PaaS Orchestrator which receives high-level deployment requests from users and coordinates the deployment process over IaaS platforms. In this work, we address an issue within INFN Cloud concerning the proliferation of INDIGO Identity and Access Management (INDIGO-IAM) clients and S3 buckets. Specifically, these resources are created during the on-demand deployment of most high-level services with no control from the INDIGO PaaS Orchestrator. This results in a scenario where, upon deployment deletion or failure, the related resources are not removed. This leads to an increasing number of unused INDIGO-IAM clients and buckets, consequently causing a decrease in the performance of the services hosting such resources. Our proposed solution involves delegating the management of such resources to the INDIGO PaaS Orchestrator, offering the users enough flexibility without losing control over them.

The INDIGO PaaS orchestration system is an open-source middleware designed to seamlessly federate heterogeneous computing environments, including public and private clouds, container platforms, and more. Its primary function lies in orchestrating the deployment of virtual infrastructures, ranging from simple to intricate setups. These virtual infrastructures can implement high-level services, such as JupyterHub, Kubernetes, Spark, and HTCondor clusters, providing users with convenient access and operational control. At the heart of the orchestration system lies its core component, the Orchestrator, supported by a suite of micro-services. These micro-services play a crucial role in assisting the Orchestrator by facilitating the selection of the optimal provider from the federated environments, based on the specific deployment request. Within this architecture, a pivotal micro-service is dedicated to implement the information system of the federation. This crucial component records comprehensive details about all the providers, encompassing their characteristics and capabilities. The information stored plays a central role in the matchmaking process between user deployment requests and available providers. Currently, this functionality is implemented by the Configuration Management Database (CMDB) service, which stores and organizes information about resource providers, and the Service Level Agreement Manager (SLAM) which retains SLAs signed by users and resource provider administrators. For instance, if a deployment request specifies the allocation of one or more Graphics Processing Units (GPUs), the Orchestrator relies on the information system to identify which providers within the federation, for which the user is entitled to allocate resources, offer GPU capabilities. We have opted to replace the existing services due to the discontinuation of maintenance for the CMDB developed during the INDIGO-DataCloud project, which relies on outdated components. The forthcoming solution, the Federation-registry, is a state-of-the-art web application built on the FastAPI framework. It features a REST API secured by OpenID-Connect/OAuth2 authentication and authorization technologies and policies. This upgrade ensures a more robust and secure foundation for managing federation-related information. The Federation-registry leverages neo4j, a highly flexible graph database, as opposed to the legacy CouchDB - a non-relational database - for storing and organizing data related to resource providers. Additionally, it adopts S3 object storage to securely store the signed SLA agreements. This upgrade promises several advantages, including improved data organization, independence from outdated and unmaintained software, adherence to test-driven code practices, enhanced flexibility for accommodating various types of providers, and simplified database structure updates for the incorporation of new provider types. This contribution will outline the architectural decisions and delve into the specifics of the implementation. The newly implemented Federation-registry service will be integrated into the INFN Cloud platform, which is already exploiting the INDIGO PaaS middleware to provide INFN scientific communities with a portfolio of high-level services supplied on-demand across geographically distributed cloud sites.