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Keynote Lectures

Climate Change: Impacts, Trends and Extremes
Ricardo Trigo, University of Lisbon, Portugal

Novel Approaches to Model Assessment and Interpretation in Geospatial Machine Learning
Alexander Brenning, Friedrich Schiller University, Germany

Structuring and Exploring Geographic Iconographic Heritage
Valérie Gouet-Brunet, IGN / Gustave Eiffel Univ., France

 

Climate Change: Impacts, Trends and Extremes

Ricardo Trigo
University of Lisbon
Portugal
 

Brief Bio
Ricardo Trigo is an Associated Professor at the Geophysics, Geographical Engineering and Energy Department and Director (between 2018 and 2021) of the Associated Laboratory Instituto Dom Luis (IDL), both at the Faculty of Sciences, Univ of Lisbon. He has published extensively (>280 papers, h=71 Scopus, h=83 Google Scholar) in complementary areas of research with particular emphasis Natural Hazards such as Floods, Droughts, Landslides, Heatwaves and Wildfires. In particular how these phenomena are related to climate change or to large-scale patterns such as NAO, Blocking or Atmospheric Rivers. In recent years he has been working increasingly on longer time scales, studying the impact of solar and volcanic variability with both proxy and early meteorological data. Ricardo Trigo has participated in more than 20 national projects, mostly funded by FCT but also by large companies related to energy (REN, EDP, E-Redes) and paper production (Portucel and the Navigator company). He has participated in 12 EU funded projects, including the ongoing ROADMAP and the recent IMDROFLOOD and INDECIS. In 2008 he was awarded with the International Journal of Climatology Prize endorsed by the Royal Met. Society and in 2017 he received the prize UL/CGD for the scientific production in Earth Sciences. In 2011 he edited the book "Hydrological, Socioeconomic and Ecological impacts of the North Atlantic Oscillation in the Mediterranean Region" published by Springer. He is currently supervising two PhD students and has supervised successfully 10 PhD students.


Abstract
The last two decades have been marked by a substantial increase in the amplitude and frequency of a wide range of climate extremes on the global scale. Such an increment presents a significant challenge with far-reaching implications for the environment, economy, and society. In particular, the European continent has been struck with more frequent extreme weather events such as heatwaves, droughts, heavy rainfall, and storms, pressing the need to understand and address the impacts of these changes. Here I provide an overview of how these extreme weather events, namely heatwaves, droughts and fires have become more frequent and to what extent we can attribute human-induced climate change to their increasing frequency (or amplitude). Addressing the growing climate extremes in Europe requires a multi-faceted approach that includes mitigation efforts to reduce greenhouse gas emissions, adaptation strategies to build resilience to changing conditions, and international cooperation to tackle this global issue. Finally, I will present a few examples where the use of high-resolution geographical information systems can be useful in informing civil protection and other authorities in developing useful adapting strategies.



 

 

Novel Approaches to Model Assessment and Interpretation in Geospatial Machine Learning

Alexander Brenning
Friedrich Schiller University
Germany
 

Brief Bio
Alexander Brenning is an applied mathematician (Technical University of Freiberg, Germany) and geographer (Ph.D., Humboldt-Universität zu Berlin) with research interests in geospatial machine learning and geostatistics for modeling of Earth surface processes, including landslide hazards, mountain permafrost, and environmental pollution. He joined Friedrich Schiller University Jena, Germany in 2015 as a Full Professor of Geographic Information Science after previously holding a faculty position at the University of Waterloo, Canada, since 2007. He has visited the University of Heidelberg as a Humboldt Research Fellow, and the Pontifical Catholic University of Chile as a Distinguished Visiting Professor, and is a member of the ELLIS Unit Jena, a research cluster in the field of machine-learning for Earth system science.


Abstract
The increasing interest in the interpretability and explainability of artificial intelligence (AI) decisions requires innovative model diagnostic tools that account for the unique challenges of geospatial and environmental data, notably spatial dependence and high dimensionality. Leveraging the geostatistical paradigm rooted in distance-based metrics, spatial prediction error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as well as derived summary statistics based on spatial cross-validation are novel, model-agnostic tools for assessing and interpreting models across various prediction horizons. Additionally, to tackle the complexities of deciphering joint effects in environments abundant with strongly correlated or high-dimensional features, interpretation tools that distill aggregated relationships from complex models are required. The efficacy of these techniques is demonstrated in two case studies: the regionalization of environmental pollution based on point measurements of concentrations, and a classification task from multitemporal remote sensing of land use. In these case studies, SPEPs and SVIPs effectively highlight differences and unexpected similarities of geostatistical methods, linear models, random forest, and blended algorithms. With 64 correlated features in the remote-sensing case study, the transformation-based interpretation approach successfully summarizes high-dimensional relationships in a small number of diagrams for effective science communication. These innovative diagnostic tools enrich the toolkit of geospatial data science, offering potential enhancements to the interpretation, selection, and design of geospatial machine-learning models.



 

 

Structuring and Exploring Geographic Iconographic Heritage

Valérie Gouet-Brunet
IGN / Gustave Eiffel Univ.
France
 

Brief Bio
Valérie Gouet-Brunet has been research director (DR1) of the French Ministry of Ecology (MTES) since 2012. She carries out her research at the French mapping agency (IGN - National Institute for Geographical and Forest Information), within the LaSTIG laboratory, and at the University Gustave Eiffel. Within the ACTE research group, she is in charge of researches on the description by content, matching and indexing of large-scale and long-term multimedia collections, with a focus on images and their structuring, exploration and spatialization with application to cultural and natural heritage. For 4 years until 2018, she headed the MATIS laboratory (35 members) at IGN, specialized in mathematics and computer science applied to photogrammetry, computer vision and remote sensing for multi-sensor and multi-source imaging. She obtained a PhD in Computer Vision in 2000 from the University of Montpellier II (France) on the area of color image matching with application to intermediate view synthesis, and an habilitation to conduct research at the Pierre and Marie Curie University (France) in 2008 on the area of content-based structuring of collections of still and animated images. V. Gouet-Brunet has supervised more than fifty PhD students and researchers and participated in or coordinated some twenty partnership projects of various kinds (French national ANR and FUI projects, European projects, bilateral industrial contracts, international research collaborations). Currently, she is coordinating the ALEGORIA project (French ANR 2018-2021), is member of the steering committee of the French Association for pattern recognition and interpretation (AFRIF), of the board of the European association Time Machine Organisation, and of the working group "Digital data" of the scientific site for the restoration of Notre-Dame de Paris.


Abstract
In every country, there exists many collections of iconographic contents that depict the territory at different time periods and different scales, such as aerial views belonging to mapping agency surveys or terrestrial photographs taken by a photograph for illustrating a place or an event. They are usually hosted by GLAMs (Galleries, Libraries, Archives, Museums) or mapping agencies, making them scattered in silo and documented or indexed with various standards depending on the hosting institution. Yet they represent a rich heritage touching many sectors of society, e.g. environmental cartography, urban planning, historians and geographers modeling the evolution of the territory, sustainable tourism, sociologists investigating public spaces, media for investigation and engagement, etc. With the acceleration of open data policies aimed at promoting the circulation and valorization of public data, it becomes easier to find out about this content. To exploit and valorize them, the main challenges remain in organizing them optimally, across an institution and even between institutions, in order to access them, discover them and visualize them in ways that are relevant to the various users. In this talk, we will revisit the paradigms and solutions that exist with the objective of structuring and exploring this growing and rich digital(ized) source of documentation of our territories, from their spatialization, their indexing with metadata and content analysis, their automatic linking up to their exploration and visualization. We will present the last advances in research from computer science, computer vision, artificial intelligence and digital humanities, and will illustrate these concepts in several domains: territory understanding from historians and sociologists point of views, collection interlinking for archives and documentation of heritage objects for restoration sites.



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