The ESSI Medal and Awards session celebrates this year's awardees for the Ian McHarg Medal and the ESSI Division Outstanding ECS Award. The Ian McHarg Medal Lecture will be given by François Robida, and the ESSI Division Outstanding ECS Award Lecture will be given by Anirudh Prabhu.

Python is one of the fastest growing programming languages and has moved to the forefront in the earth system sciences (ESS), due to its usability, the applicability to a range of different data sources and, last but not least, the development of a considerable number ESS-friendly and ESS-specific packages.

This interactive Python course is aimed at ESS researchers who are interested in adding a new programming language to their repertoire. Except for some understanding of fundamental programming concepts (e.g. loops, conditions, functions etc.), this course presumes no previous knowledge of and experience in Python programming.

The goal of this course is to give the participants an introduction to the Python fundamentals and an overview of a selection of the most widely-used packages in ESS. The applicability of those packages ranges from (simple to advanced) number crunching (e.g. Numpy), to data analysis (e.g. Xarray, Pandas) to data visualization (e.g. Matplotlib).

The course will be grouped into different sections, based on topics discussed, packages introduced and field of application. Furthermore, each section will have an introduction to the main concepts e.g. fundamentals of a specific package and an interactive problem-set part.

This course welcomes active participation in terms of both on-site/virtual discussion and coding. To achieve this goal, the i) course curriculum and material will be provided in the form of Jupyter Notebooks ii) where the participants will have the opportunity to code up the iii) solutions to multiple problem sets and iv) have a pre-written working solution readily available. In these interactive sections of the course, participants are invited to try out the newly acquired skills and code up potentially different working solutions.

We very much encourage everyone who is interested in career development, data analysis and learning a new programming to join our course.

Foundation Models (FM) represent the next frontier in Artificial Intelligence (AI). These generalized AI models are designed not just for specific tasks but for a plethora of downstream applications. Trained on any sequence data through self-supervised methods, FMs eliminate the need for extensive labeled datasets. Leveraging the power of transformer architectures, which utilize self-attention mechanisms, FMs can capture intricate relationships in data across space and time. Their emergent properties, derived from the data, make them invaluable tools for scientific research. When fine-tuned, FMs outperform traditional models, both in efficiency and accuracy, paving the way for rapid development of diverse applications. FMs, with their ability to synthesize vast amounts of data and discern intricate patterns, can revolutionize our understanding of and response to challenging global problems, such as monitoring and mitigating the impacts of climate change and other natural hazards.

The session will discuss advances, early results and best practices related to the preparation and provisioning of curated data, construction and evaluation of model architectures, scaling properties and computational characteristics of model pretraining, use cases and finetuning of downstream applications, and MLops for the deployment of models for research and applications. The session also encourages discussion on broad community involvement toward the development of open foundation models for science that are accessible for all.

Modern challenges of climate change, disaster management, public health and safety, resources management, and logistics can only be addressed through big data analytics. A variety of modern technologies are generating massive volumes of conventional and non-conventional geospatial data at local and global scales. Most of this data includes geospatial data components and is analysed using spatial algorithms. Ignoring the geospatial component of big data can lead to an inappropriate interpretation of extracted information. This gap has been recognised and led to the development of new spatiotemporally aware strategies and methods.

This session discusses advances in spatiotemporal machine learning methods and the software and infrastructures to support them.

Geospatial artificial intelligence (GeoAI) has gained popularity for creating maps, performing analyses, and developing geospatial applications that are national, international or global in scope, thanks to its capacity to process and understand large geospatial data and infer valuable patterns and information. Rapid geo-information updates, public safety improvement, smart city developments, green deal transition as well as climate change mitigation and adaptation are among the problems that can now be studied and addressed using GeoAI.
Along with the acceleration of GeoAI adoption, a new set of implementation challenges is ascending to the top of the agenda for leaders in mapping technologies. These challenges relate to “operationalizing large GeoAI systems”, including automating the AI lifecycle, tracking and adapting models to new contexts and landscapes, temporal and spatial upscaling of models, improving explainability, balancing cost and performance, creating resilient and future-proof AI and IT operations, and managing activities across Cloud and on-premise environments.
This session aims to provide a venue to present the latest applications of GeoAI for mapping at national, international and global scales as well as their operationalization challenges. The themes of the session include, but are not limited to:
· Requirements of GeoAI methods for national mapping agencies, their relationship with industrial/commercial stakeholders, and the role of national agencies in establishing GeoAI standards.
· GeoAI interoperability and research translation.
· Extracting core geospatial layers and enhancing national basemaps from multi-scale, multi-modal remote-sensing data sources.
· Large-scale point cloud analysis for use cases in infrastructure development, urban planning, forest inventory, energy consumption/generation modeling, and natural resources management.
· Measuring rates and trends of changes in landscape patterns and processes such as land-cover/land-use change detection and disaster damage proxy mapping.
· Modernizing national archives, including geo-referencing, multi-temporal co-registration, super-resolution, colorization, and analysis of historical air photos.

The recent growing number of probes in the heliosphere and future missions in preparation led to the current decade being labelled as "the golden age of heliophysics research". With more viewpoints and data downstreamed to Earth, machine learning (ML) has become a precious tool for planetary and heliospheric research to process the increasing amount of data and help the discovery and modelisation of physical systems. Recent years have also seen the development of novel approaches leveraging complex data representations with highly parameterised machine learning models and combining them with well-defined and understood physical models. These advancements in ML with physical insights or physically informed neural networks inspire new questions about how each field can respectively help develop the other. To better understand this intersection between data-driven learning approaches and physical models in planetary sciences and heliophysics, we seek to bring ML researchers and physical scientists together as part of this session and stimulate the interaction of both fields by presenting state-of-the-art approaches and cross-disciplinary visions of the field.

The "ML for Planetary Sciences and Heliophysics" session aims to provide an inclusive and cutting-edge space for discussions and exchanges at the intersection of machine learning, planetary and heliophysics topics. This space covers (1) the application of machine learning/deep learning to space research, (2) novel datasets and statistical data analysis methods over large data corpora, and (3) new approaches combining learning-based with physics-based to bring an understanding of the new AI-powered science and the resulting advancements in heliophysics research.
Topics of interest include all aspects of ML and heliophysics, including, but not limited to: space weather forecasting, computer vision systems applied to space data, time-series analysis of dynamical systems, new machine learning models and data-assimilation techniques, and physically informed models.

Deep Learning has seen accelerated adoption across Hydrology and the broader Earth Sciences. This session highlights the continued integration of deep learning and its many variants into traditional and emerging hydrology-related workflows. We welcome abstracts related to novel theory development, new methodologies, or practical applications of deep learning in hydrological modeling and process understanding. This might include, but is not limited to, the following:

(1) Development of novel deep learning models or modeling workflows.

(2) Probing, exploring and improving our understanding of the (internal) states/representations of deep learning models to improve models and/or gain system insights.

(3) Understanding the reliability of deep learning, e.g., under non-stationarity and climate change.

(4) Modeling human behavior and impacts on the hydrological cycle.

(5) Deep Learning approaches for extreme event analysis, detection, and mitigation.

(6) Natural Language Processing in support of models and/or modeling workflows.

(7) Uncertainty estimation for and with Deep Learning.

(8) Applications of Large Language Models (e.g. ChatGPT, Bard, etc.) in the context of hydrology.

(9) Advances towards foundational models in the context of hydrology and Earth Sciences more generally.

(10) Exploration of different optimization strategies, such as self-supervised learning, unsupervised learning, and reinforcement learning.

Proper characterization of uncertainty remains a major research and operational challenge in Environmental Sciences and is inherent to many aspects of modelling impacting model structure development; parameter estimation; an adequate representation of the data (inputs data and data used to evaluate the models); initial and boundary conditions; and hypothesis testing. To address this challenge, methods that have proved to be very helpful include a) uncertainty analysis (UA) that seeks to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through the model, and b) the closely-related methods for sensitivity analysis (SA) that evaluate the role and significance of uncertain factors in the functioning of systems/models.

This session invites contributions that discuss advances, both in theory and/or application, in (Bayesian) UA methods and methods for SA applicable to all Earth and Environmental Systems Models (EESMs), which embrace all areas of hydrology, such as classical hydrology, subsurface hydrology and soil science.

Topics of interest include (but are not limited to):
1) Novel methods for effective characterization of sensitivity and uncertainty
2) Novel methods for spatial and temporal evaluation/analysis of models
3) Novel approaches and benchmarking efforts for parameter estimation
4) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)
5) The role of information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with no data etc.)
6) Methods for evaluating model consistency and reliability as well as detecting and characterizing model inadequacy
7) Analyses of over-parameterised models enabled by AI/ML techniques
8) Robust quantification of predictive uncertainty for model surrogates and machine learning (ML) models
9) Approaches to define meaningful priors for ML techniques in hydro(geo)logy

The invited speaker of this session is Francesca Pianosi (University of Bristol).

The complexity of hydrological systems poses significant challenges to their prediction and understanding capabilities. The rise of machine learning (ML) provides powerful tools for modeling these intricate systems. However, realizing their full potential in this field is not just about algorithms and data, but requires a cooperative interaction between domain knowledge and data-driven power. This session aims to explore the frontier of this convergence, examining how prior understanding of hydrological and land surface processes or causal representations can be incorporated into data-driven models, and conversely, how ML might enrich our causal or physical understanding of system dynamics and mechanisms.

We invite researchers working at the intersection of explainable ML/AI and hydrological or Earth system sciences to share their methods, results, and insights. Submissions are welcome on topics including, but not limited to:

- Explainability and transparency in ML/AI modeling of hydrological and Earth systems;
- Integration of hydrological processes and knowledge in ML/AI models;
- Multiscale and multiphysics representation in ML/AI models;
- Causal representation learning in hydrological and earth systems;
- Strategies for balancing model performance and interpretability;
- Leveraging insights from data science and XAI to deepen physical understanding;
- Data-driven approaches to causal analysis in hydrological and Earth systems;
- Challenges, limitations, and solutions related to hybrid models and XAI.

This groundbreaking session merges the forefront of digital technology and explainable artificial intelligence (XAI) to redefine our approach to natural hazard management and resilience. As natural hazards such as earthquakes, floods, landslides, and wildfires become more frequent and severe, leveraging advanced digital solutions is crucial. This session delves into the synergistic application of remote sensing, machine learning, geographic information systems (GIS), IoT, quantum computing, digital twins, and VR/AR in understanding, predicting, and managing natural disasters.
We place a special emphasis on the role of eXplainable AI (XAI) in demystifying AI-driven predictive models. By exploring algorithms like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), we aim to make AI predictions in natural hazard assessment transparent and trustworthy. This approach not only enhances the predictive accuracy but also fosters trust and understanding among stakeholders.
Attendees will gain insights into cutting-edge research and practical applications, showcasing how these integrated technologies enable real-time monitoring, early warning systems, and effective communication strategies for disaster management. The session will feature case studies highlighting the successful application of these technologies in diverse geographic regions and hazard scenarios. This interdisciplinary platform is dedicated to advancing our capabilities in mitigating the risks and impacts of natural hazards, paving the way for safer, more resilient communities in the face of increasing environmental challenges.

Time series are a very common type of data sets generated by observational and modeling efforts across all fields of Earth, environmental and space sciences. The characteristics of such time series may however vastly differ from one another between different applications – short vs. long, linear vs. nonlinear, univariate vs. multivariate, single- vs. multi-scale, etc., equally calling for specifically tailored methodologies as well as generalist approaches. Similarly, also the specific task of time series analysis may span a vast body of problems, including
- dimensionality/complexity reduction and identification of statistically and/or dynamically meaningful modes of (co-)variability,
- statistical and/or dynamical modeling of time series using stochastic or deterministic time series models or empirical components derived from the data,
- characterization of variability patterns in time and/or frequency domain,
- quantification various aspects of time series complexity and predictability,
- identification and quantification of different flavors of statistical interdependencies within and between time series, and
- discrimination between mere correlation and true causality among two or more time series.
According to this broad range of potential analysis goals, there exists a continuously expanding plethora of time series analysis concepts, many of which are only known to domain experts and have hardly found applications beyond narrow fields despite being potentially relevant for others, too.

Given the broad relevance and rather heterogeneous application of time series analysis methods across disciplines, this session shall serve as a knowledge incubator fostering cross-disciplinary knowledge transfer and corresponding cross-fertilization among the different disciplines gathering at the EGU General Assembly. We equally solicit contributions on methodological developments and theoretical studies of different methodologies as well as applications and case studies highlighting the potentials as well as limitations of different techniques across all fields of Earth, environmental and space sciences and beyond.

The world is witnessing a transformation of long-held paradigms in the face of unprecedented grand environmental and social challenges. These complex, interconnected issues demand collaborative, innovative, and data-driven approaches. International scientific infrastructures play a pivotal role in advancing research on these challenges by facilitating data sharing, promoting FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and upholding CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) principles. This session invites abstracts from scientists, developers, and decision-makers to explore how international scientific infrastructures are shaping the future of research and decision-making in the geosciences and beyond.
We invite research and insights into the role and progress of AI/ML, open science, FAIR principles, governance, collaborative research, and ethical data sharing as applied to climate research and modeling, dynamic satellite mapping of the Earth's surface, 3D/4D mapping of the subsurface, early warning systems, water security, capacity building, and the evaluation of impact.

Workflow methodologies and systems are fundamental tools for scientific experimentation, especially when complex computational systems such as distributed or high-performance computing are required, to improve scientific productivity and meet criteria essential for reproducibility and provenance of results.

Recent advances and upcoming developments in Earth System Science (ESS) are facing the challenge of having to i) efficiently handle close-to exascale data amounts and ii) providing methods to make the information content readily accessible and usable by both scientists and downstream communities.

Concurrently, awareness of the importance of the reproducibility and replicability of research results has increased considerably in recent years. Reproducibility refers to the possibility of independently arriving at the same scientific conclusions. Replicability or replication, is achieved if the execution of a scientific workflow arrives at the same result as before.

A sensible orchestration of these two aspects requires the application of seamless workflow tools employed at compute and data infrastructures which also enable the capture of required provenance information to - in an extreme case - rerun large-simulations and analysis routines to provide trust in model fidelity, data integrity and decision-making processes. Here, reproducibility, or even replicability, dedication to Open Science and FAIR data principles are key. Further, this enables communities of practice to establish best practices in applying future-proof workflows among a critical mass of users, thereby facilitating adoption.

This session discusses latest advances in workflow techniques for ESS in a two-tiered organizational structure, focusing on:

- sharing use cases, best practices and progress from various initiatives that improve different aspects of these technologies, such as eFlows4HPC (Enabling dynamic and Intelligent workflows in the future EuroHPC ecosystem), Climate Digital Twin (Destination Earth), or EDITO (European Digital Twin Ocean) Model-Lab;

-current approaches, concepts and developments in the area of reproducible workflows in ESS, like requirements for reproducibility and replicability including provenance tracking; technological and methodological components required for data reusability and future-proof research workflows; FAIR Digital Objects (FDOs); (meta)data standards, linked-data approaches, virtual research environments and Open Science principles.

Advances in computational capacities, technologies in modelling and information systems and increasing availability of observational data have given rise to ideas to apply the concept of digital twins to the Earth system and its components. Different projects or initiatives are now working to develop information systems that not only employ and advance state-of-the-art simulation systems, but also allow their users to interact with these systems more directly, e.g. by configuring or initiating simulations, coupling models with additional data streams and workflows, visualizing simulations interactively. Applications in a wide variety of sectors stand to benefit from these developments, including disaster risk management, climate adaptation, agriculture and forestry, renewable energy, public health management, and others.

This session invites contributions on current developments in Digital Twin initiatives. These may focus on technology developments and challenges, data management, interactivity tools, or application demonstrations.

Research data infrastructures (RDIs) serve to manage and share research products in a systematic way to enable research across all scales and disciplinary boundaries Their services support researchers in data management and collaborative analysis throughout the entire data lifecycle.

For this fostering of FAIRness and openness, e.g. by applying established standards for metadata, data, and/or scientific workflows, is crucial. Through their offerings and services, RDIs can shape research practices and are strongly connected with the communities of users that identify and associate themselves with them.

Naturally, the potential of RDIs faces many challenges. Even though it is clear that RDIs are indispensable for solving big societal problems, their wide adoption requires a cultural change within research communities. At the same time RDIs themselves must be developed further to serve user needs. And, also at the same time, the sustainability of RDIs must be improved, international cooperation increased, and duplication of development efforts must be avoided. To be able to provide a community of diverse career stages and backgrounds with a convincing infrastructure that is established beyond national and institutional boundaries, new collaboration patterns and funding approaches must be tested so that RDIs foster cultural change in academia and be a reliable foundation for FAIR and open research. This needs to happen while academia struggles with improving researcher evaluation, with a continuing digital disruption, with enhancing scholarly communication, and with diversity, equity, and inclusion.

In Earth System Science (ESS), several research data infrastructures and components are currently developed on different regional and disciplinary scales , all of which face these challenges at some level. solutions
This session provides a forum to exchange methods, stories, and ideas to enable cultural change and international collaboration in scientific communities, to bridge the gap between user needs, and to build sustainable software solutions.

Cloud computing has emerged as the dominant paradigm, supporting practically all industrial applications and a significant number of academic and research projects. Since its inception and subsequent widespread adoption, migrating to cloud computing has presented a substantial challenge for numerous organisations and enterprises. Leveraging cloud technologies to process big data in proximity to their physical location represents an ideal use case. These cloud resources provide the requisite infrastructure and tools, especially when accompanied by high-performance computing (HPC) capabilities.

Pangeo (pangeo.io) is a global community of researchers and developers that tackle big geoscience data challenges in a collaborative manner using HPC and Cloud infrastructure. This session's aim is threefold:
(1) Focuses on Cloud/Fog/Edge computing use cases and aims to identify the status and the steps towards a wider cloud computing adoption in Earth Observation and Earth System Modelling.
(2) to motivate researchers who are using or developing in the Pangeo ecosystem to share their endeavours with a broader community that can benefit from these new tools.
(3) to contribute to the Pangeo community in terms of potential new applications for the Pangeo ecosystem, containing the following core packages: Xarray, Iris, Dask, Jupyter, Zarr, Kerchunk and Intake.

We warmly welcome contributions that detail various Cloud computing initiatives within the domains of Earth Observation and Earth System Modelling, including but not limited to:
- Cloud federations, scalability and interoperability initiatives across different domains, multi-provenance data, security, privacy and green and sustainable computing.
- Cloud applications, infrastructure and platforms (IaaS, PaaS SaaS and XaaS).
- Cloud-native AI/ML frameworks and tools for processing data.
- Operational systems on the cloud.
- Cloud computing and HPC convergence and workload unification for EO data processing.

Also, presentations using at least one of Pangeo’s core packages in any of the following domains:
- Atmosphere, Ocean and Land Models
- Satellite Observations
- Machine Learning
- And other related applications

We welcome any contributions in the above themes presented as science-based in other EGU sessions, but more focused on research, data management, software and/or infrastructure aspects. For instance, you can showcase your implementation through live executable notebooks.

As the urgency of addressing complex global challenges such as climate change, biodiversity loss, and sustainable resource management increases, the role of exploiting and producing high-quality, high-resolution geospatial information in these efforts is becoming increasingly crucial. Google Earth Engine has emerged as a powerful and well-established tool for harnessing the potential of these data products, reducing reliance on desktop and in-house computational platforms and providing researchers and developers with a compelling cloud-based alternative for planetary-scale geospatial analysis.

This session invites contributions from developers, researchers, and users providing cloud based solutions to key problems which push the boundaries of what is possible with Google Earth Engine. We welcome submissions focusing on, but not limited to:

Novel applications and case studies demonstrating the use of Earth Engine in addressing real-world problems
Generation and visualization of new databases and remote sensing products customized to specific applications
Development of new tools, extensions, or apps that enhance the functionality of Earth Engine
Methodological innovations in Earth Engine, including the implementation of advanced geospatial algorithms
Efforts to integrate Earth Engine with other data sources or analytical tools
Discussions on challenges, lessons learned, and future directions in the use of Earth Engine for geospatial analysis.

Whether you are using Earth Engine to map deforestation, predict flood risks, track disease spread, or develop new analytical tools, this session is your opportunity to share your work, learn from others, and explore the future of geospatial analysis with Google Earth Engine.

Data plays a crucial role in driving innovation and making informed decisions. The European strategy for data is a vision of data spaces that aims to foster creativity and open data, while also prioritizing personal data protection, consumer safeguards, and FAIR principles. Satellite imagery has transformative potential, but limitations of data size and access have previously constrained applications.

Additional thematic or geographical data spaces are being developed, such as the European sectorial data spaces, the Copernicus Data Space Ecosystem and Green Deal Data Space. These provide access to high-quality, interoperable data, through streamlined access, on-board processing and online visualization generating actionable knowledge and supporting more effective decision-making. These novel tools of this digital ecosystem create a vast range of opportunities for geoscience research, development and communication at local to global scale. Operational applications such as monitoring networks and early warning systems are built on top of these infrastructures, facilitating governance and sustainability in the face of global challenges. Worldwide satellite imagery data series can be accessed through API systems, creating analysis ready data for advanced machine learning applications. Put together, these advances in data availability, analysis tools and processing capacity are transformative for geoscience research. There is a growing demand for a deeper understanding of their design, establishment, integration, and evolution within the environmental and Earth sciences. As a geoscience community, it is imperative that we explore how data spaces can revolutionize our work and actively contribute to their development.

This session connects developers and users of the Copernicus Data Space Ecosystem and other European satellite data infrastructures and Data Spaces, showing how data spaces facilitate the sharing, integration, and flexible processing of environmental and Earth system data from diverse sources. The speakers will discuss how ongoing efforts to build data spaces will connect with existing initiatives on data sharing and processing, and present examples of innovative services that can be developed using data spacess. By leveraging these cutting-edge tools within the digital ecosystem, the geoscience community will gain access to a vast range of opportunities for research, development, and communication at local and global scales.

Earth System Models (ESMs) have evolved considerably in complexity, capability and scale as evidenced in projects such as the Coupled Model Intercomparison Project Phase 6 and the forthcoming CMIP7 project.
Coupled Earth system interactions such as feedbacks and potential abrupt changes are a significant source of uncertainty in our current understanding of the Earth system and how it might respond to future human interventions.
There is therefore a need to credibly assess such developments and capabilities for effective research on climate variability and change.
This session will examine physical, biogeochemical and biophysical processes likely to affect the evolution of the Earth system over the coming decades and centuries. Contributions with a focus on; (a) the latest advances in the representation of these couplings and interactions within state-of-the-art numerical models; (b) novel experimental designs to help improve quantification of these feedbacks, including those targeting CMIP7 activities and (c) novel approaches for benchmarking and evaluation of ESMs including cross-domain and process -based evaluation, observational uncertainties, science and performance metrics and benchmarks; are all particularly welcome.
This session arises from the joint initiative of the The CMIP7 Model Benchmarking Task Team, EU-funded ESM2025 and OptimESM projects.

In recent decades, the use of geoinformation technology has become increasingly important in understanding the Earth's environment. This session focuses on modern open-source software tools, including those built on top of commercial GIS solutions, developed to facilitate the analysis of mainly geospatial data in various branches of geosciences. Earth science research has become more collaborative with shared code and platforms, and this work is supported by Free and Open Source Software (FOSS) and shared virtual research infrastructures utilising cloud and high-performance computing. Contributions will showcase practical solutions and applications based on FOSS, cloud-based architecture, and high-performance computing to support information sharing, scientific collaboration, and large-scale data analytics. Additionally, the session will address the challenges of comprehensive evaluations of Earth Systems Science Prediction (ESSP) systems, such as numerical weather prediction, hydrologic prediction, and climate prediction and projection, which require large storage volumes and meaningful integration of observational data. Innovative methods in open frameworks and platforms will be discussed to enable meaningful and informative model evaluations and comparisons for many large Earth science applications from weather to climate to geo in the scope of Open Science 2.0.

In the Environmental and Solid Earth research fields, addressing complex scientific and societal challenges with holistic solutions within the dynamic landscape of data-driven science underscores the critical need for data standardisation, integration and interoperability. Just as humans communicate effectively to share insights, machines must seamlessly exchange data. The high-capacity computing services allow for the discovery and processing of large amounts of information, boosting the integration of data from different scientific domains and allowing environmental and solid Earth research to thrive on interdisciplinary collaboration and on the potential of big data.
As earth and environmental researchers, our expertise is essential in addressing natural and ecological problems, which extends to our engagement with operational infrastructures (the Environmental Research Infrastructures-ENVRIs, the European Open Science Cloud-EOSC, the EGI Federation, among others). Data repositories, e-service providers and other research or e-infrastructures support scientific development with interoperability frameworks and technical solutions, to effectively bridge the traditional boundaries between the disciplines, and enhance machine-to-machine (M2M) interactions, enabling data and service interoperation. 
Join this session to explore real-world examples from earth and environmental scientists (from atmosphere, marine, ecosystems or solid earth), data product developers, data scientists and engineers. Whether you've navigated infrastructures, addressed data analytics, visualisation and access challenges, or embraced the transformative potential of digital twins. Whether you've gained expertise in data collection, quality control and processing, employed infrastructures to expedite your research, or participated in Virtual Access and/or Transnational Access programs to expand your horizons. We invite researchers with diverse expertise in data-driven research to showcase impactful scientific use cases and discuss interdisciplinary methodologies or propose best practices with successful interoperability frameworks. Join us as we explore ways to enhance the FAIRness of earth and environmental data, fostering open science within and beyond our fields. 

The United Nations (UN) 2030 Agenda for Sustainable Development set a milestone in the evolution of society's efforts towards sustainable development which must combine social inclusion, economic growth, and environmental sustainability. The definition of the Sustainable Development Goals (SDGs) and the associated Global Indicator Framework represent a data-driven framework helping countries in evidence-based decision-making and development policies.

Earth observation (EO) data, including satellite and in-situ networks, and EO data analytics and machine learning plays a key role in assessing progress toward meeting the SDGs, since it can make the 2030 Agenda monitoring and reporting viable, technically and financially and be beneficial in making SDG indicators' monitoring and reporting comparable across countries.

This session invites contributions on how to make use of Earth Observations data to address SDG monitoring and reporting, in particular welcomes presentations about EO-driven scientific approaches, EO-based tools, and EO scientific initiative and projects to build, assess and monitor UN SDGs indicators.

Understanding Earth's natural processes, particularly in the context of global climate change, has gained widespread recognition as an urgent and central research priority that requires further exploration. Recent advancements in satellite technology, characterized by new platforms with high revisit times and the growing capabilities for collecting repetitive ultra-high-resolution aerial images through unmanned aerial vehicles (UAVs), have ushered in exciting opportunities for the scientific community. These developments pave the way for developing and applying innovative image-processing algorithms to address longstanding and emerging environmental challenges.
The primary objective of the proposed session is to convene scientific researchers dedicated to the field of satellite and aerial time-series imagery. The aim is to showcase ongoing research efforts and novel applications in this dynamic area. This session is specifically focused on presenting studies centred around the creation and utilization of pioneering algorithms for processing satellite time-series data, as well as their applications in various domains of remote sensing, aimed at investigating long-term processes across all Earth's realms, including the sea, ice, land, and atmosphere.
In today's era of unprecedented environmental challenges and the ever-increasing availability of data from satellite and aerial sources, this session serves as a platform to foster collaboration and knowledge exchange among experts working on the cutting edge of Earth observation technology. By harnessing the power of satellite and aerial time-series imagery, we can unlock valuable insights into our planet's complex systems, ultimately aiding our collective efforts to address pressing global issues such as climate change, natural resource management, disaster mitigation, and ecosystem preservation.
The session organizers welcome contributions from researchers engaged in applied and theoretical research. These contributions should emphasize fresh methods and innovative satellite and aerial time-series imagery applications across all geoscience disciplines. This inclusivity encompasses aerial and satellite platforms and the data they acquire across the electromagnetic spectrum.

Earth's dynamic and complex environmental systems are continually evolving, driven by various natural and anthropogenic factors. In an era marked by increasing hazards, dwindling natural resources, and the undeniable effects of climate change, harnessing the power of cutting-edge technologies to address these critical issues at the local level is needed. To monitor and understand these changes, scientists increasingly rely on time series remote sensing data and advanced artificial intelligence (AI) geospatial tools. The session aim will be to showcase the latest advancements in remote sensing technology and geospatial software that facilitate the monitoring and analyzing local hazards, natural resources, and climate change impacts. We will discuss case studies and research findings demonstrating the effectiveness of time series remote sensing data in addressing specific local challenges. The goal will be to foster interdisciplinary collaboration among researchers, policymakers, and practitioners to develop actionable strategies for addressing local issues.
The proposed session aims to bring together experts and researchers from diverse disciplines to explore innovative approaches and solutions using time series aerial and satellite remote sensing data combined with geospatial technology software. The session will delve into the practical applications of these technologies in understanding and mitigating local challenges related to hazards, natural resources, and climate change.
The conveners’ welcome contributions from interdisciplinary scientists, educators, innovators, policy makers and local stakeholders in applied and theoretical domains, emphasizing innovative methodologies and practical applications of time-series imagery from satellites and aerial sources to explore innovative solutions for tackling local challenges related to hazards, natural resources, and climate change. We encourage using data acquired across the electromagnetic spectrum (optical and SAR) worldwide via aerial and satellite platforms.

The visualization and user-friendly exploration of information from scientific data is one of the main tasks of good scientific practice. But steady increases in temporal and spatial resolutions of modeling and remote sensing approaches lead to ever-increasing data complexity and volumes. On the other hand, earth system science data are getting increasingly important as decision support for stakeholders and other end users far beyond the scientific domains.

This poses major challenges for the entire process chain, from data storage to web-based visualization. For example, (1) the data has to be enriched with metadata and made available via appropriate and efficient services; (2) visualization and exploration tools must then access the often decentralized tools via interfaces that are as standardized as possible; (3) the presentation of the essential information must be coordinated in co-design with the potential end users. This challenge is reflected by the active development of tools, interfaces and libraries for modern earth system science data visualization and exploration.

In this session, we hence aim to establish a transdisciplinary community of scientists, software-developers and other experts in the field of data visualization in order to give a state-of-the-art overview of tools, interfaces and best-practices. In particular, we look for contributions in the following fields:

- Developments of open-source visualization and exploration techniques for earth system science data
- Co-designed visualization solutions enabling transdisciplinary research and decision support for non-scientific stakeholders and end-users
- Tools and best-practices for visualizing complex, high-dimensional and high frequency data
- Services and interfaces for the distribution and presentation of metadata enriched earth system science data
- Data visualization and exploration solutions for decentralized research data infrastructures

All contributions should emphasize the usage of community-driven interfaces and open-source solutions and finally contribute to the FAIRification of products from earth system sciences.

Humans have been successfully mapping the remotest and most inhospitable places on Earth, and the surfaces and interiors of other planets and their moons at highest resolution. The remaining blank spots are located in areas that are hardly accessible either through field surveys, geophysical methods or remote sensing due to technical and/or financial challenges. Some of these places are key areas that would help to reveal geologic history, or provide access to future exploration endeavours.

Such extreme and remote locations are commonly associated with the ocean floor, or planetary surfaces, but these extreme worlds might also be found in hot deserts, under the ice, in high-mountain ranges, in volcanic edifices, hidden underneath dense canopy cover, or located within the near-surface crust. All such locations are prime targets for remote sensing mapping in a wider sense. The methodological and technical repertoire to investigate extreme and remote locations is thus highly specialized and despite different contexts there are commonalities not only with respect to technical mapping approaches, but also in the way how knowledge is gathered and assessed, interpreted and visualized regarding its scientific but also its economic value.

This session invites contributions to this field of geologic mapping and cartography of extreme (natural) environments with a focus on the scientific synthesis and extraction of information and knowledge.

A candidate contribution might cover, but is not limited to, topics such as:

- ocean mapping using manned and unmanned vehicles and devices,
- offshore exploration using remote sensing techniques,
- crustal investigation through drilling and sampling,
- subsurface investigation using radar techniques,
- planetary geologic and geophysical mapping,
- subglacial geologic mapping
- geologic investigation of desert environments.

The aim of this session is to bring together researchers mapping geologically and geophysically inaccessible environments, thus relying on geophysical and remote sensing techniques as single source for collecting data and information. We would like to keep the focus on geologic and geophysical mapping of spots for which we have no or only very limited knowledge due to the harsh environmental conditions, and we would thus exclude areas that are inaccessible for political reasons.

Remote sensing products have a high potential to contribute to monitoring and modelling of water resources. Nevertheless, their use by water managers is still limited due to lack of quality, resolution, trust, accessibility, or experience.
In this session, we look for new developments that support the use of remote sensing data for water management applications from local to global scales. We are looking for research to increase the quality of remote sensing products, such as higher resolution mapping of land use and/or agricultural practices or improved assessments of river discharge, lake and reservoir volumes, groundwater resources, drought monitoring/modelling and its impact on water-stressed vegetation, as well as on irrigation volumes monitoring and modeling. We are interested in quality assessment of remote sensing products through uncertainty analysis or evaluations using alternative sources of data. We also welcome contributions using a combination of different techniques (physically based models or artificial intelligence techniques) or a combination of different sources of data (remote sensing and in situ) and different imagery types (satellite, airborne, drone). Finally, we wish to attract presentations on developments of user-friendly platforms providing smooth access to remote sensing data for water applications.
We are particularly interested in applications of remote sensing to determine the human water interactions and the climate change impacts on the whole water cycle.