. . . "Classify, explain and analyse the factors underlying the hazards and evaluate their relative importance."@en . . "Classify, explain and analyse the factors underlying the hazards and evaluate their relative importance."@en . . . "Critically asses susceptibility maps and the approaches to convert them into hazard maps."@en . . "Critically asses susceptibility maps and the approaches to convert them into hazard maps."@en . . . "In the context of landslide hazard, formulate the data requirements for the statistical modelling approaches (including aspects of scale and data quality)."@en . . "In the context of landslide hazard, formulate the data requirements for the statistical modelling approaches (including aspects of scale and data quality)."@en . . . "Understand the appropriate use, the limitations and the issues one can tackle through statistical modelling for predictive mapping of hazards. Implement this knowledge through scripting data-driven methods for hazard prediction."@en . . "Understand the appropriate use, the limitations and the issues one can tackle through statistical modelling for predictive mapping of hazards. Implement this knowledge through scripting data-driven methods for hazard prediction."@en . . . "In the context of landslide hazard, formulate the data requirements to automatically map inventories through Google Earth Engine."@en . . "In the context of landslide hazard, formulate the data requirements to automatically map inventories through Google Earth Engine."@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "The Master’s Programme Geo-Information Science and Earth Observation (M-GEO) is a two-year academic curriculum at MSc level, taught fully in English, dedicated to understanding the earth’s systems from a geographic and spatial perspective. The field of Geo-information Science and Earth Observation has, in recent years, witnessed fast scientific and technological developments. As a result, geographic information has become a vital asset to society and part of our daily life. The ubiquitous production and availability of spatial data require cloud computing and new technology to turn the increasing volume of ‘big data’ to good use. The growing range of global challenges, from climate change and resource depletion to environmental pollution and pandemic diseases, that our society and in particular the more vulnerable populations on our planet are facing, increases the demand for academic professionals who have the ability, attitudes and skills to design solutions that are sustainable, transdisciplinary and innovative with positive societal impacts. Our education focuses on addressing these global problems by means of advanced geo-information and earth observation applications."@en . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . . . . . . . . . . . "Natural Hazards and Disaster Risk Reducation"@en . "NHR"@en . . . "Course"@en . "201800282" . "NHR_002" . "7"^^ . "196"^^ . "10"^^ . "2023-02-02T23:00:00Z"^^ . "f2f" . "online" . "This course focuses on building the required understanding of natural hazards and the available approaches to map them and further predict their occurrence in space and time. This knowledge will be systematically acquired through short theoretical lectures followed by supervised practicals and tutorials that will expose students to the whole conceptual and modeling pipeline, from cloud-based inventory-making to data acquisition and ultimately to susceptibility and hazard assessment. To promote and make a constructive use of the diversity in the background of the students, each step of the course will also feature a peer-learning process where students with different training will share their knowledge to mutually benefit from each respective understanding of the lessons. At the end of each day, interactive quiz will be provided to monitor the growth of each student and provide support where needed. The learning process will be further supported by a group project assignment that will link together the content of the course. In fact, the automated mapping and the modelling techniques will be implemented and critically assessed in terms of their specific limitations and with respect to the final goal (inventory generation and susceptibility/hazard mapping)."@en . ",Compulsory for the “Natural Hazards and Disaster Risk Reduction” (NHR) specialization of the “Geoinformation Science and Earth Observation” (M-GEO) programme. Students from other specializations and programmes should have introductory level experience with GIS and Remote Sensing, and a background in earth sciences, geography, environmental science or civil engineering.\n"@en . "3"^^ . "2" . "1B" . "2022-11-13T23:00:00Z"^^ . "The identification and assessment of natural hazards is a crucial component of disaster risk management. This course will focus on the modelling of natural hazards, with an emphasis on hydro-meteorological hazards (floods, landslides and erosion). Starting from the relevant natural phenomena and their causes, the generation of historical inventories of hazardous phenomena will be discussed. From the cloud-based generation of the hazard inventories and their interpretation, the course will expand on the main methods and tools to assess the susceptibility and hazard at different scales. The course will provide the foundation for predictive approaches with a particular focus given to statistical models of multivariate nature. The latter will combine the spatial and temporal dimensions. The use of empirical models will further investigate runout patterns to estimate areas under threat.\n\nThe course runs in parallel to the \"Introduction to Hazard and Risk\" course (Q2.1) where data input for hazard modelling are explained. The two course are closely coupled and part of the necessary knowledge for the \"Data-Driven Hazard modeling\" course will be gained in parallel through lessons and concepts explained in Q2.1."@en . "Data-driven Hazard Modelling"@en . "Data-driven Hazard Modelling"@en . "Data-driven Haz