. . "7" . "5.0" . "140.0" . "10.0" . . . . . "F2F" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . "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 . . "59" . "5.0" . "140.0" . "10.0" . . . . . "blended (F2F and online)" . . . . . . . . . . . . . . . . "Open for all students with an interest in weather and weather data processing, with a background in earth sciences, physical geography, water resources, natural resources, natural hazards, soil science, engineering."@en . "No"@en . "No"@en . . . . . . "4.0" . "3.0" . "Weather is everywhere. The weather has an impact on the earth surface, and on everything that is on that surface: vegetation, soil, water availability, humans, etc. Many natural hazards have extreme weather conditions and events as a trigger, like droughts, floods, heat-waves, and rainfall-induced landslides. For example, agricultural production is dependent on weather conditions, as extreme weather events, like a tropical cyclone, might cause irreversible damage to crop or to land, and lead to less harvest. Similarly, the extent and magnitude of the urban heat island effects are largest under hot, stable weather conditions, causing severe health impact. And, as global climate change poses huge challenges to society as these extreme weather conditions are increasing in severity and frequence, we have to understand the relation between weather and natural hazards.\n\nFortunately, the weather is continuously monitored worldwide, by satellites and ground stations at minute, daily or monthly scales. As well weather is observed in various meteorological parameters. Many meteorological datasets are freely accessible, being an enormously rich source for weather information. Long time series of these weather parameters allow us to build climate information services; how did the weather and extreme events change in the past? Similarly, the output of various climate models is freely accessible on a worldwide scale. When analyzing and visualising this weather and climate dataset, one gets insight into the various weather conditions and extreme events, that are potentially linked to natural hazards, now and in the future. \n\nThis course provides knowledge on weather data sources and tools to analyze the interaction between the weather and earth surface processes in time and space. The challenge will be to link this climatic information to non-meteorological data to learn how hazards might be changing under climate change conditions."@en . "Weather & Climate"@en . . "Weather & Climate"@en . "Weather & Climate"@en . . "45" . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . "MSc research and thesis writing"@en . . "MSc research and thesis writing"@en . "MSc research and thesis writing"@en . . "30" . "5.0" . "140.0" . "10.0" . . . . . . "online" . . . . . . . . . . . . . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . . "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 . "Fundamental Spatial Data Engineering and Innovations"@en . . "Fundamental Spatial Data Engineering and Innovations"@en . "Fundamental Spatial Data Engineering and Innovations"@en . . "40" . "5.0" . "140.0" . "10.0" . "blended (F2F and online)" . "No"@en . "No"@en . . . . . . . "2.0" . "This course will provide a fundamental introduction to natural hazards and the disaster risk concept, as well as the role of geomatics, in particular remote sensing (RS). It builds on the knowledge students gained in the foundation courses on basic RS and GIS principles as well as statistical methods, and expands it. The course aims at creating a knowledge base for the other hazard modelling and risk management courses and electives in the Disaster Resilience thematic line, by enabling the students to develop an understanding of the main geohazard types and their - mainly geomorphological - origins, and all relevant conceptual aspects of disaster risk. Students will learn how geo-information and geomatics tools are uniquely suited to study, monitor and quantify each aspect of risk and disasters. Following an introduction to the main hazard types and their core properties, students will work in groups to dissect past disaster events to discover the nature and properties of the underlying hazards and vulnerabilities, and learn how in particular RS provides comprehensive and specifically tailored means to gain insights into the risk components for different hazards and environmental settings. The course is mandatory for all 3 specialisations within Disaster Resilience (managing, modelling, data analysis), and is closely coupled with the course Introduction to Data-Driven Hazard Modelling (Q2.2). Particular attention will be given to the generation of input data for hazard modelling, including image-based indices and topographic derivatives, and information extracted from UAV/drone imagery. Relevant background information on soils, geology and landforms as drivers of hazards will also be provided. The course concludes with a section on risk reduction and resilience creation concepts."@en . "Introduction to Hazard, Risk & Resilience, and the Role of Geodata"@en . . "Introduction to Hazard, Risk & Resilience, and the Role of Geodata"@en . "Introduction to Hazard, Risk & Resilience, and the Role of Geodata"@en . . "38" . "5.0" . "140.0" . "10.0" . . . . . "blended, F2F, online" . . . . . . . . . . . . . . . "Introduction to hazard risk resillience course, GIS data management/terrain analysis"@en . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@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 . "Introduction to Data-driven Hazard Modelling"@en . . "Introduction to Data-driven Hazard Modelling"@en . "Introduction to Data-driven Hazard Modelling"@en . . "31" . "5.0" . "140.0" . "10.0" . . . . . "online" . . . . . . . . . . . . "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 specifics needed. For other cases, the candidates will be assessed on an individual basis."@en . "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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "Yes"@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.\tIntroduction 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.\tRS 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.\tGIS Basics and Data Handling: Interlinked with RS, comes the introduction of GIS principles, spatial data models, data acquisition and visualization. Students will engage in hands-on exercises to collect, input, and manage and visualize spatial data, learning essential techniques like digitizing, GPS, and attribute data collection.\n4.\tImage Interpretation and Classification: Students will gain skills in interpreting RS imagery, performing both visual analysis and supervised image classification, and assessing classification accuracy.\n5.\tSpatial Analysis and Integration: The course integrates RS data with GIS to enhance spatial analysis capabilities. Students will practice GIS techniques in combination with RS data in different processing flows.\n6.\tPractical Applications: Along the course students are exposed to RS and GIS techniques linked to different thematic lines. "@en . "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 . "Fundamentals of GIS and Earth Observation"@en . . "Fundamentals of GIS and Earth Observation"@en . "Fundamentals of GIS and Earth Observation"@en . . "20" . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . "Academic Research Skills"@en . . "Academic Research Skills"@en . "Academic Research Skills"@en . . "46" . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . "MSc research proposal writing"@en . . "MSc research proposal writing"@en . "MSc research proposal writing"@en . . "29" . "5.0" . "140.0" . "10.0" . . . . . . "online" . . . . . . . . . . . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . . "1.0" . "In an increasingly data-driven world, proficiency in data analysis and geospatial data processing has become a crucial skill set. The course “From Statistics to Programming and Machine Learning” is designed to equip students with the fundamental tools and techniques required to navigate the complex landscapes of data analysis, descriptive statistics, geospatial data handling and foundational machine learning techniques using Python. Python, with its extensive libraries and versatility, is the go-to language for professionals in data science, geography, environmental science, urban planning, and beyond.\n\nThis 5 EC course offers a comprehensive introduction to Python, starting with the basics of programming and gradually progressing to more advanced topics like descriptive statistics, data visualisation, and geospatial data processing and Machine Learning. With Jupyter Notebooks, an interactive environment widely used by data professionals, students will gain hands-on experience in writing Python code, analysing data, and visualising results.\n\nThe course is structured into five modules, each focusing on a critical aspect of Python for data analysis. Beginning with Python programming fundamentals, students will build a strong foundation before moving on to descriptive statistics and data visualisation techniques. The course then delves into geospatial data, where students will learn to handle and analyse vector and raster data, followed by a module on advanced manipulation and visualisation of tabular data. The course culminates in an integrated project where students will apply their knowledge to solve a real-world problem, combining geospatial and tabular data for a comprehensive analysis.\n\nBy the end of this course, students will not only have mastered the essential skills in Python programming and data analysis but also be prepared to tackle complex data-driven challenges in GIS and Earth observation."@en . "Fundamental Programming for Geospatial Data Analysis"@en . . "Fundamental Programming for Geospatial Data Analysis"@en . "Fundamental Programming for Geospatial Data Analysis"@en . . . . . . . . . . . . . . . . . . . . . "Disaster Resilience"@en . . . . "Modeler/Mod