. . "5.0" . "140.0" . "10.0" . . . . . "F2F" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "4.0" . "3.0" . "The aim of this course is to enhance the student’s understanding of the physical processes that cause natural hazards, the methods and the physically-based modelling approaches for hazard analysis, to the point at which students are able to use them with their own data. As the processes of selected natural hazards, including flooding, landslides and earthquakes, are explained, the students will be introduced to fundamentals of the underpinning science and engineering. Model data requirements and data collection will be treated, as well as the evaluation of uncertainty of input data on simulation outputs. Modelling principles and assumptions, possibilities and limitations will be discussed with the aim that students can make a proper selection of models for a given situation and critically reflect on the results, in order to support hazard analysis as input to risk management and mitigation. "@en . "Physically-based Hazard Modelling"@en . . "Physically-based Hazard Modelling"@en . "Physically-based Hazard Modelling"@en . . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . . . . . "1.0" . "Geospatial problem solving for addressing societal challenges employs a wide variety of theories, methods, and tools, each applicable to a specific type or aspect of the problem solving process. All approaches, however, involve the acquisition, processing, and dissemination of data in one form or another. The Geoinformation and Earth Observation specialist must, therefore, be equipped with the necessary skills to find, use, preserve, and disseminate geospatial data. This course introduces conceptual models, analysis tools, and infrastructure for representing and analysing geographic phenomena in computer systems. The course covers both spatial and temporal aspects of the observed phenomena. Fundamental concepts of spatial representation including geometric primitives, topology, multidimensionality, spatial autocorrelation, graphs and networks, will be introduced in the context spatial data management. By the end of the course students should be able to interact with local or remote data resources using a variety of technologies including SQL and common web service APIs (e.g OGC WMS, WFS, WCS, REST). The student should therefore become familiar with common of data formats used in GIS and EO. Students will also learn to apply elementary data transformations (analysis) to obtain data in the appropriate structure for dissemination and presentation in both static and dynamic spatiotemporal visualizations. Applications in urban and land futures planning will be used in examples and exercises throughout this course. Learning units are organized so that concepts and methods from various knowledge categories are combined into a wholistic skill set that a student can use to solve a specific geoapstial problem."@en . "Spatial Methods & Data Management"@en . . "Spatial Methods & Data Management"@en . "Spatial Methods & Data Management"@en . . "5.0" . "140.0" . "10.0" . . . . . . "2.0" . "Introduction to hazard risk resilience"@en . . "Introduction to hazard risk resilience"@en . "Introduction to hazard risk resilience"@en . . "5.0" . "140.0" . "10.0" . . . . . "blended, F2F, online" . . . . . . . . . . . . . . . "Introduction to hazard risk resillience course, GIS data management/terrain analysis"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "3.0" . "2.0" . "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 (e.g., 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."@en . "Data driven Hazard modelling"@en . . "Data driven Hazard modelling"@en . "Data driven Hazard modelling"@en . . "5.0" . "140.0" . "10.0" . . . . . . . . "Open for students in the Master of Science degree programme in Geo-Information Science and Earth Observation. As first Foundational course, the pre-requisites align to those of acceptance to the M-GEO program, consequently there are no other specifcs needed. For other cases, the candidates will be assessed on an individual basis."@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "1.0" . "This 5 ECTS course is designed for students aiming to acquire foundational knowledge and skills in these critical geospatial technologies. The course provides a comprehensive introduction to the principles, techniques, and applications of remote sensing (RS) and Geographic Information Systems (GIS), emphasizing their integration to solve real-world problems.\nCourse Structure and Content\n1. Introduction to RS and GIS: The course begins with an overview of RS and GIS, exploring their, core concepts, and significance in various fields such as environmental monitoring, urban planning, agriculture, and disaster management.\n2. RS Fundamentals: Students will learn about different types of RS systems, focusing on optical passive sensors included in satellite and airborne platforms, as well as the electromagnetic spectrum's role in data acquisition. Participants are exposed to key preprocessing steps such as radiometric and geometric corrections ensuring data quality.\n3. GIS Basics and Data Handling: Interlinked with RS, comes the introduction of GIS principles, spatial data models, and database management. Students will engage in hands-on exercises to collect, input, and manage spatial data, learning essential techniques like digitizing, GPS, and attribute data collection.\n4. Image Interpretation and Classification: Students will gain skills in interpreting RS imagery, performing both visual analysis and supervised image classification, and assessing classification accuracy.\n5. Spatial Analysis and Integration: The course integrates RS data with GIS to enhance spatial analysis capabilities. Students will practice GIS analytical techniques, such as buffer and overlay analysis, combining data acquire from geoportals and processing flows.\n6. Practical Applications and Project Work: Along the course there is a “cumulative” project-based learning of choice, where students are exposed to RS and GIS techniques of at least one learning path, while practicing the instructed methodology. Collaborative projects and specific case studies will reinforce theoretical knowledge and practical skills, preparing students for professional applications."@en . "GIS & EO foundation"@en . . "GIS & EO foundation"@en . "GIS & EO foundation"@en . . "5.0" . "140.0" . "10.0" . . . . . . "3.0" . "Risk and resilience management/risk assessment"@en . . "Risk and resilience management/risk assessment"@en . "Risk and resilience management/risk assessment"@en . . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . . . . . "1.0" . "Visualisation and Communication"@en . . "Visualisation and Communication"@en . "Visualisation and Communication"@en . . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . . . . . "1.0" . "From Statistics to Programming and Machine Learning"@en . . "From Statistics to Programming and Machine Learning"@en . "From Statistics to Programming and Machine Learning"@en . . . . . . . . . . . . . . . . "Disaster Resilience"@en . . "Modeler/Modelin