. . "27" . "Yes"@en . "No"@en . . "Economics and finances for geosciences"@en . . "Economics and finances for geosciences"@en . "Economics and finances for geosciences"@en . . "6" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Basic GIS and Remote Sensing skills"@en . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . "5.0" . "2.0" . "Global change, caused by growing population densities and rising economic production levels, is increasingly placing pressure on scarce land resources. These changes do not always contribute to sustainable development and often increase the pressure on the natural resources that we depend on. Our impact on the environment is immense, and we are fast approaching several tipping points. Without proper management, these environments and the natural resources they provide will be depleted and degraded, sometimes irreversibly. They will no longer be able to provide society with essential services (water, food, carbon sequestration, temperature and rainfall regulation, pest regulation etc.)."@en . "Natural Resources Management Fundamentals"@en . . "Natural Resources Management Fundamentals"@en . "Natural Resources Management Fundamentals"@en . . "18" . "2.5" . "70.0" . "5.0" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . "3.0" . "This course is meant for students who want to get into the field of geospatial data acquisition, processing, and application using state-of-the-art spaceborne, airborne, and terrestrial sensors and technologies.\nThis course provides students with significant knowledge on how to utilize active and passive imaging sensors, and laser scanning for collecting high-resolution 3D geospatial data. The main use of the 3D geoinformation obtained through this course is the creation of Digital Twins. By understanding and implementing Digital Twins, students will be able to enhance decision-making in urban planning, infrastructure maintenance, environmental conservation, and emergency response, etc.\nDuring the course, students will investigate aircraft and drone vehicles that are equipped with imaging sensors and laser scanners (LiDAR) for creating highly accurate 3D models of terrain and structures. Students will learn the benefits of the integration of 3D products from photogrammetry and laser scanning and how it will create more precise 3D geoinformation for engineering applications, spatial analysis, and 3D visualization. "@en . "3D Geoinformatics for Engineering applications"@en . . "3D Geoinformatics for Engineering applications"@en . "3D Geoinformatics for Engineering applications"@en . . "48" . "Yes"@en . "No"@en . . "Project Management & Collaborative Skills"@en . . "Project Management & Collaborative Skills"@en . "Project Management & Collaborative Skills"@en . . "54" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . . "Basic programming"@en . . . . . . "No"@en . "No"@en . . . . . . . . . "2.0" . "2.0" . . . "With the advancements in LLM and AI it becomes important to not just b able to programme or code, but also to make appropriate use of these new technologies to develop reproducible code."@en . "Scientific Programming for Geosciences"@en . . "Scientific Programming for Geosciences"@en . "Scientific Programming for Geosciences"@en . . "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 . . "42" . "Yes"@en . "No"@en . . "Land Use Change Modelling"@en . . "Land Use Change Modelling"@en . "Land Use Change Modelling"@en . . "15" . "5.0" . "140.0" . "10.0" . . . . . . "blended" . . . . . . . . . . . . . . . . "Basic statistical analyses, Spatial statistics"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . "3.0" . "3.0" . "Understanding urban dynamics and urban growth is crucial for strategic long-term planning of infrastructure, economic development, environmental sustainability, social equity and overall urban resilience. At its core, the interaction between land use and transportation plays a pivotal role in shaping urban dynamics, and such interactions and dynamics can be most efficiently understood by modelling.\nModelling urban dynamics and growth involves the use of various theoretical frameworks that captures transportation infrastructure affects land use patterns and vice versa. In this course, the students will not only be introduced with theories about land use and transportation interactions, but also knowledges and techniques of implementing models that encodes the interactions quantitatively. Several modelling frameworks (to be specified) will be introduced to simulate travel decisions and behaviours, mobility and accessibility, land use land cover changes. On top of developing the modelling capacity, the students will also be trained to assess and interpret the modelled scenarios, so that to link the modelling into the practical context of urban planning and policy making."@en . "Urban Futures Modelling"@en . . "Urban Futures Modelling"@en . "Urban Futures Modelling"@en . . "25" . "Yes"@en . "No"@en . . "Digital Participatory Planning"@en . . "Digital Participatory Planning"@en . "Digital Participatory Planning"@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 . . "9" . "5.0" . "140.0" . "10.0" . . . . . . "F2F" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . "6.0" . "3.0" . "Both hazard types and frequency, as well as built-up areas and cities are dynamically changing, resulting from climate and global changes. In April 2024, displacing 600.000 people in Brazil due to floods, having hottest day records already in Europe and in Asia are clear examples to the shifting hazard patterns. In such dynamic environments, the interdependency among the risk components amplifies the impact of disasters. In such an environment, disaster risk is constantly changing, and there is a definite limit to our capacity to foresee the failures resulting from unexpected interactions between interdependent components. Indeed, the intensity and extent of the challenges make clear that achieving resilient cities is everybody’s business. Scientists, stakeholders and citizens are faced with the challenge to adapt their disaster risk reduction plans but lack the understanding and tools to account for the cross-sectoral impacts and dynamic nature of the risks involved. In this course, we follow the socio-technical approach in complex city systems and investigate the ways to contribute to cities’ resilience. The main problem in disaster risk management is providing static measures to a dynamically changing system. In this course you will learn looking at the nature of risk as a 'dynamic' concept rather than a static one. You will focus on multi-hazard risk assessment and dynamic risk reduction measures on various sectors."@en . "Planning for Resilient Cities"@en . . "Planning for Resilient Cities"@en . "Planning for Resilient Cities"@en . . "43" . "Yes"@en . "No"@en . . "Land Use Transport Interactions"@en . . "Land Use Transport Interactions"@en . "Land Use Transport Interactions"@en . . "45" . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . "MSc research and thesis writing"@en . . "MSc research and thesis writing"@en . "MSc research and thesis writing"@en . . "2" . "2.5" . "70.0" . "5.0" . . . . . . . . . . . . . "blended" . . . . . . . . . . . . . "operational GIS skills"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . "1.0" . "3.0" . "Geospatial database engineering is the professional suite of activities that is needed to\ndevelop, realize and maintain a usually large database system that holds geospatial data\nsources that service usually a sizeable user community.\nWherever large geospatial datasets, especially comprising vector data, are shared between\nprofessional users the technology to apply is a geospatially enabled database management\nsystem (sdbms). Its purpose is to serve as a reliable data store for its community of users,\nand provide a resource of agreed upon and documented quality.\nThis course teaches how to initiate such a database system, bring in external data, curate\nthe data, and put in place guards against data incorrectness, invalidity, incompleteness\nand inconsistency.\nIt next addresses how conceptual descriptions of functions that must become part of the\nsystem's application programming interface can be implemented using the programming\nfacilities that the sdbms offers. We look into the coding paradigm of set-based\nprogramming, and make use of mathematical logic and comprehension schemes, which are\ntypical of SQL.\nSpecific attention will be paid in this course to computing with geospatial vector data.\nThis also requires understanding of the OGC Simple Feature model, ISO 19125. Various\ntechniques will be introduced to test and validate, correct and improve vector data, and\nwe discuss a number of typical problem situations and template solutions to them.\nThe course will bring to the student understanding of how to approach these challenges and\nskills to resolve them."@en . "Geospatial Database Engineering"@en . . "Geospatial Database Engineering"@en . "Geospatial Database Engineering"@en . . "58" . "2.5" . "70.0" . "5.0" . . . . . "blended" . . . . . . . . . . . . . . . "None"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . "2.0" . "2.0" . "The management of space is one of the big challenges that human societies have to deal with. Competing claims over the use of finite resources have pushed human societies to develop institutions, technologies and paradigms that help manage these competing usages. Challenges like increasing urbanization, the depletion of land and other natural resources, and climate change make this management of space ever more urgent.\nThe governance of land and urban development is essential when considering development approaches to support sustainable futures. As a concept, governance encapsules (1) Multi level co-ordination and multi-faceted problems; (2) Multi actor networks, and (3) Multi-instrumental steering mechanisms. This implies that an understanding of problems, actors and steering mechanisms involved in the governance of land and urban development is necessarily focused on the context in which a certain problem is placed and how it can be addressed by the governance settings available.\nIn this course we focus on key concepts of land and urban governance. The aim is for the student to gain a background in (challenges of) governance of land and urban development, that will influence how individuals, organizations and institutions work towards land and urban futures. An additional challenge in the course is inviting the students to reflect, discuss and imagine different land and urban futures. "@en . "Urban and Land Futures"@en . . "Urban and Land Futures"@en . "Urban and Land Futures"@en . . "33" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . "Earth processes and Society (Q2)"@en . "No"@en . "No"@en . . . . "4.0" . "3.0" . . "[CONCEPT] Earth observation satellites generate large amounts of geospatial data that are freely available for society and researchers. Technologies such as cloud computing and distributed systems are modern solutions to access and process big Earth observation data. This course is on processing remote sensing data from operational and historic missions in an online platform, with a specific emphasis on earth science applications. The application to Earth sciences will help you to recognize landforms in images, determine Earth's surface composition and derive various physical parameters from the Earth's surface."@en . "Geological remote sensing for regional mapping"@en . . "Geological remote sensing for regional mapping"@en . "Geological remote sensing for regional mapping"@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 . . "47" . "5.0" . "140.0" . "10.0" . . . . . "f2f / blended / online " . . . . . . . . . . . . . . . "Foundation Courses, introduction to Urban Land Futures, Data sharing, Data engineering"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . "4.0" . "3.0" . "Land administration has long been executed through state-based agencies such as cadastral departments,\nland registry offices, ministries of land, or local governments with their own analogue or digital data\nrepositories. These organizations do not act in a vacuum but within larger institutional fields and forces.\nThe broader environment of land governance, in which public organizations operate, is characterized by\nthe interactions of multiple state and non-state actors, formal and informal practices, a multitude of\nregulatory frameworks and increasing global interconnectivity. This environment has been witnessing\npublic sector reforms and increased adoption of (geo)Information and Communication Technologies (ICT),\nincluding automatization techniques, mobile device-generated data, crowdsourcing and advanced remote\nsensing technologies. In many places, more established forms of organizing meet the latest technological\ndevelopments. While some organizations are beginning to digitize paper-based workflows, others may\nfunction through highly automated and digitized processes. At the same time, information technologies and\ndigital data are not merely neutral tools, but they reflect, transport and transform the practices and values\nof organizations and institutional fields.\nIt is important therefore to understand and learn how to describe, explain, and assess organizational\nchange in response to changing environments, (geo-)ICT implementation using workflows and related\nforms of data sharing, uses and dissemination."@en . "Organizing Land Information in Practice "@en . "Organizing Land Information in Practice"@en . . "Organizing Land Information in Practice "@en . "Organizing Land Information in Practice"@en . "Organizing Land Information in Practice "@en . "Organizing Land Information in Practice"@en . . "0" . "5.0" . "140.0" . "10.0" . . . . . . "F2F" . . . . . . . . . . . . . . . . "Machine Learning for Geosciences or equivalent"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . "4.0" . "4.0" . "CONCEPT (not yet confirmed by all GEO-AI staff):\nBuilding on the foundations laid in the Machine Learning for Geospatial Sciences course, this advanced program delves into the forefront of machine learning and deep learning technologies tailored for geospatial applications. Here, you will explore sophisticated models and techniques that enable the analysis of both spatial and spatio-temporal data, addressing complex real-world challenges with precision and insight.\nThe course begins with an in-depth exploration of advanced machine learning algorithms, designed to handle the unique complexities of geospatial data. You will learn to apply these algorithms to model intricate spatial patterns and relationships, enhancing your ability to derive meaningful insights from diverse geospatial datasets.\nDeep learning algorithms, known for their capacity to process large and complex datasets, will be a significant focus. You will master techniques for analyzing geospatial imagery, recognizing patterns, and making accurate predictions. Algorithms like Recurrent NN, Transformers and Graph NN empower you to extract detailed and valuable information from high-resolution and divers geospatial data.\nHandling spatio-temporal data requires specialized approaches. You will learn advanced methods for analyzing time series data, predicting temporal changes in geospatial phenomena, and help understanding the dynamics of processes such as weather patterns, urban growth, and environmental shifts.\nState-of-the-art architectures and methods will be introduced, highlighting their remarkable ability to capture and model complex dependencies in data. You will explore their application in geospatial sciences, particularly in tasks requiring attention mechanisms to focus on relevant spatial regions or temporal sequences.\nAn important component of this course is Explainable AI (XAI), ensuring transparency and interpretability of your models. You will learn techniques to make complex models understandable, fostering trust and facilitating informed decision-making in geospatial applications. We will discuss the ethical implications of AI in geospatial sciences, emphasizing the importance of responsible data use, privacy concerns, and the societal impact of AI-driven decisions.\nBy the end of this course, you will be proficient in leveraging advanced machine learning and deep learning techniques for geospatial sciences, equipped to tackle sophisticated spatial and spatio-temporal challenges ethically and transparently. Join us to advance your expertise and contribute to the transformative power of AI in geospatial sciences!"@en . "Advanced Machine Learning for Geospatial Sciences"@en . . "Advanced Machine Learning for Geospatial Sciences"@en . "Advanced Machine Learning for Geospatial Sciences"@en . . "4" . "5.0" . "140.0" . "10.0" . . . . . . . . "F2F" . . . . . . . . . . . . . . . . . . "Statistics, calculus, linear algebra, analytics geometry, programming (Python)"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . "4.0" . "3.0" . "CONCEPT (not yet confirmed by all GEO-AI staff):\nThis course is designed to guide you through the intersection of machine learning and geospatial sciences, providing you with the expertise to address pressing societal and environmental challenges. You will be introduced to the foundations of supervised and unsupervised learning algorithms, exploring their applications in the geospatial domain. You will learn popular learning algorithms to address various inference tasks, such as clustering, regression and classification.\nFrom satellite imagery to GIS datasets you'll master the tools and methodologies required to preprocess, analyze, integrate and visualize them. You will gain the skills needed to extract meaningful patterns and insights from these geospatial datasets.\nFeature extraction and engineering are critical steps in building effective machine learning models. You will explore techniques to transform raw geospatial data into relevant features enabling your models to learn and predict more effectively.\nClustering techniques, for exploratory spatial data analysis, will be introduced to help you to discover hidden structures and trends within geospatial datasets.\nClassification and regression methods like decision trees, random forests, support vector machines and neural networks are pivotal machine learning tasks that you'll apply to a wide array of geospatial problems. Whether it's land use classification, predicting environmental changes, or estimating spatial variables like temperature or population density, you'll develop models that provide precise and actionable insights.\nThroughout the course real-world case studies will demonstrate the transformative impact of machine learning on geospatial sciences. You'll work on projects that tackle contemporary issues such as urban planning, environmental monitoring, and disaster management.\nBy the end of this course, you will be adept at applying machine learning techniques to geospatial sciences."@en . "Machine Learning for Geospatial Sciences"@en . . "Machine Learning for Geospatial Sciences"@en . "Machine Learning for Geospatial Sciences"@en . . "41" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . "MGEO - foundation course"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . . "5.0" . "4.0" . "Fieldwork is often an essential component to acquire reference data for calibration and validation of the remotely sensed observations. This course provides skills and techniques to plan, execute and report on field observations. The course starts with an introduction to in-situ field measurement devices and lab equipment, and demonstrates standard operational procedures when analysing samples in the laboratory. Subsequently, students have to design their own field data collection based on a self-defined objective, e.g. which parameters are required and how to conduct sampling, which instruments are required, how to measure, sampling procedures and storing of samples. Considering focus group interviews how to prepare the questionnaires and review of ethical considerations. Another element would be the timing related to eventual satellite overpass or image acquisition in the terrain and collection of available information from installed in-situ measuring devices. \n\nBeing well prepared, a 3 day fieldwork is envisaged for practical collecting data in a fieldwork area with participants from multiple disciplines: water, natural and earth resources. \n\nOnce back, the data collected has to be analysed in the lab or subject to further processing. In the end, students are required to present their results obtained and have to report on the procedures applied, reflect on the quality of obtained results, and describe their analysis conducted into more detail. "@en . "Lab & Field Work Skills "@en . "Lab & Field Work Skills"@en . . "Lab & Field Work Skills"@en . "Lab & Field Work Skills "@en . "Lab & Field Work Skills"@en . . "56" . "Yes"@en . "No"@en . . "Spatial analyses of ecosystem services"@en . . "Spatial analyses of ecosystem services"@en . "Spatial analyses of ecosystem services"@en . . "11" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . "Foundation, CORE Book"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . . "4.0" . "2.0" . "Remote sensing is a unique tool to observe the Earth system, and to quantitatively monitor a variety of key atmospheric, land and ocean variables by measuring radiation reflected or emitted by the earth or atmosphere. With the availability of more and more remote sensing data from various types of instruments with different spectral characteristics, temporal and spatial resolutions, the field of quantitative land remote sensing is advancing rapidly. This course provides an overview of Earth Observation from Space by describing basic concepts of orbits and viewing from space, instrument characteristics as well as exploring the electromagnetic radiation ranges used by remote sensing devices, like in the VIS, NIR, SWIR, TIR atmospheric windows and active and passive Microwave regions, but also within atmospheric absorption bands. Radiative transfer equation and atmospheric correction for signal correction are discussed and practised. \n\nAttention is given to space and ground segments, operational (meteorological) satellite programmes within the ocean and sea ice, land and atmospheric domains and the retrieval of various space based observations of geophysical variables and their availability in cloud repositories and online processing platforms, and their retrieval.\n\nAlso attention is given to calibration and validation, related to instrument calibration (before launch, on board and vicarious calibration) but also to bias adjustment of long term data records and the need of validation when using the geophysical variables obtained through space based observations. "@en . "Quantitative Remote Sensing 5 EC Resource Security"@en . . "Quantitative Remote Sensing 5 EC Resources Security"@en . "Quantitative Remote Sensing 5 EC Resource Security"@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 . . "52" . "Yes"@en . "No"@en . . "Remote Sensing in the Context of Climate Change"@en . . "Remote Sensing in the Context of Climate Change"@en . "Remote Sensing in the Context of Climate Change"@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 . . "22" . "2.5" . "70.0" . "5.0" . . . . . "blended (F2F and online)" . . . . . "foundation courses"@en . "No"@en . "No"@en . . . . "3.0" . "2.0" . "Much of the MGEO disaster resilience thematic line focuses on hazards and risk, especially on relevant concepts and different modelling approaches. Together this builds the ability to assess multi-hazard risk in a given area, and to model specific hazard processes. The attention of this present course is on actual disaster events, specifically on how to anticipate them to minimize the adverse consequences, and on how to improve post-event response and recovery. In terms of preparing for an actual event we will focus on the concept of early action/ anticipatory action, focusing on the different actors and their roles, but also relevant early warning systems that provide information on impending, in particular hydrometerological events. The technical and organisational aspects of converting early warning information into impact assessments will be addressed, and how such information is used in preestablished trigger models that set in motion last-minute activities on the ground that can help communities prepare for the disaster event, and reduce losses. Immediate disaster response, in particular damage assessment, is already addressed in the Introduction to Hazard, Risk & Resilience course. Here we will focus on the subsequent disaster recovery, addressing how affected communities can learn from the event and build-back-better, and what role different stakeholders play in this process. We will further review how geoinformation, in particular remote sensing data, can be used to assess and characterise both physical and functional recovery."@en . "Anticipating and Responding to Disasters"@en . . "Anticipating and Responding to Disasters"@en . "Anticipating and Responding to Disasters"@en . . "17" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . "CORE MODULE"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . "4.0" . "2.0" . "Water and energy are fundamental for life on Earth, their variations, trends, and extremes are sources for drought extremes, heat waves, heavy rains, floods, and intensive storms that are increasingly threatening our society to cause havoc as the climate changes. Better observations and analysis of these phenomena will help improve our ability to understand their physical processes and to model and predict them. Earth Observation technology is a unique tool to provide a global understanding of essential water and energy variables and monitor their evolution from global to basin scales. The focus of this course is on the physical principles of how electromagnetic signals are applied to monitor these essential variables by spaceborne sensors, and learn tools and methods to collect, process, and visualize Earth observation data of surface solar radiation, evapotranspiration, precipitation, soil moisture, and terrestrial water storage. Furthermore, students will learn how to retrieve the essential water/climate variable – soil moisture from Earth observation data, applying the radiative transfer theory."@en . "Water Cyle in the anthropocene"@en . . "Water Cyle in the anthropocene"@en . "Water Cyle in the anthropocene"@en . . "55" . "Yes"@en . "No"@en . . "Space for ethics"@en . . "Space for ethics"@en . "Space for ethics"@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 . . "1" . "2.5" . "70.0" . "5.0" . . . . . . "blended" . . . . . . . . . . . . . . . . "Basic land and urban futures concepts (course ULF 1), Spatial data handling (courses FC 1 and FC2), basic programming concepts (course FC3)"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . "4.0" . "3.0" . "With expanding urban settlements, increasing demand for resources, and exacerbating environmental challenges, the complexity of future urban and regional systems is expected to increase. Information systems for planning and managing land use policy implementation will therefore become indispensable tools in the urban planning and land administration toolboxes. In this course students learn to think in systems terms and use systems analysis and design methods to not only describe the functionality of an information system but, perhaps more importantly, to describe the data, information structures, processes, states, and state evolutions of interest within the urban and/or regional system under consideration. The course introduces the software process as a project implementation methodology. Analysis and design approaches are introduced in the context of this overarching structure. First students will learn to analyse requirements documents to conceptualize a system's purpose and boundary. Additional information from the domain and requirements documents will be used to develop a conceptual model of the domain and identify user actions and processes within the domain. UML class diagrams will be used to structure concepts. UML use case diagrams and activity diagrams will be used to analyse user intentions, actions, and the information system's responses. Finally UML state machines will allow student to describe the set of states that can be occupied by all or part of the system being modelled. The modeling constructs introduced are applicable to both the information system and the real world domain. Examples will help clarify how to apply the tool in both contexts. "@en . "Designing Urban & Land Information Systems"@en . . "Designing Urban & Land Information Systems"@en . "Designing Urban & Land Information Systems"@en . . "10" . "2.5" . "70.0" . "5.0" . "Foundation, CORE Book"@en . "No"@en . "No"@en . . . . . . . . . "4.0" . "2.0" . "Remote sensing is a unique tool to observe the Earth system, and to quantitatively monitor a variety of key atmospheric, land and ocean variables by measuring radiation reflected or emitted by the earth or atmosphere. With the availability of more and more remote sensing data from various types of instruments with different spectral characteristics, temporal and spatial resolutions, the field of quantitative land remote sensing is advancing rapidly. This course provides an overview of Earth Observation from Space by describing basic concepts of orbits and viewing from space, instrument characteristics as well as exploring the electromagnetic radiation ranges used by remote sensing devices, like in the VIS, NIR, SWIR, TIR atmospheric windows and active and passive Microwave regions, but also within atmospheric absorption bands. Radiative transfer equation and atmospheric correction for signal correction are discussed and practised. \n\nAttention is given to space and ground segments, operational (meteorological) satellite programmes within the ocean and sea ice, land and atmospheric domains and the retrieval of various space based observations of geophysical variables and their availability in cloud repositories and online processing platforms, and their retrieval.\n\nAlso attention is given to calibration and validation, related to instrument calibration (before launch, on board and vicarious calibration) but also to bias adjustment of long term data records and the need of validation when using the geophysical variables obtained through space based observations. "@en . "Quantitative Remote Sensing 2.5 EC GeoAI"@en . . "Quantitative Remote Sensing 2.5 EC GeoAI"@en . "Quantitative Remote Sensing 2.5 EC GeoAI"@en . . "21" . "Yes"@en . "No"@en . . "Advanced Geospatial Analysis & Time Series"@en . . "Advanced Geospatial Analysis & Time Series"@en . "Advanced Geospatial Analysis & Time Series"@en . . "39" . "Yes"@en . "No"@en . . "Introduction to entrepreneurship"@en . . "Introduction to entrepreneurship"@en . "Introduction to entrepreneurship"@en . . "14" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . "previous ULF courses or similar knowledge and experiences"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . "5.0" . "4.0" . "Addressing current and future societal challenges or urban areas around the world requires integrating thinking and insights where space, society and technology intersect. This new geo-socio-technical approach to of solving urban and land problem demands a new way of working and indeed re-conceiving of the tools and methods that inform our solutions to these challenges. Advancements in planning support and decision making technologies have enabled evidence-based scenario planning but failed in engaging a broad range of non-experts in future-oriented planning practice that accounts for deep uncertainty and complexity of societal challenges.\nIn this studio course, student groups engage in challenge-based learning of a real-world spatial problem setting. Geospatial and participatory technologies for systematic analysis of locational phenomena and spatial characteristics will be applied in combination with methods for eliciting local experiential knowledge of residents and other societal actors to disentangle wicked problem settings and underlying root causes and to develop visions of a sustainable urban and land future. Goal of studio-based learning approach is to provide a policy making authority with integrated insights and inspiration for new methods for producing sage, and to co-design together with them future-oriented strategies and interventions in an inclusive manner.\nIn this course students are exposed to various lab facilities of ITC and learn how to make use of them for data collection, stakeholder interaction and collaborative planning and decision making. "@en . "Urban and Land Futures Studio"@en . . "Urban and Land Futures Studio"@en . "Urban and Land Futures Studio"@en . . "49" . "Yes"@en . "No"@en . . "Qualitative research methodologies "@en . . "Qualitative research methodologies "@en . "Qualitative research methodologies "@en . . "44" . "5.0" . "140.0" . "10.0" . "Yes"@en . "No"@en . . "3.0" . "Airborne, terrestrial and mobile laser scanning are modern technologies to acquire and monitor the\ngeometry of the Earth's surface, objects above the surface like buildings, trees and road infrastructure,\nand even building interiors. This course provides an overview of the state of the art of these techniques,\npotential applications, like digital terrain modelling and 3D city modelling, as well as methods to extract geoinformation from the recorded point clouds."@en . "Laser Scanning"@en . . "Laser Scanning"@en . "Laser Scanning"@en . . "5" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . . "4.0" . "3.0" . "[CONCEPT] Monitoring of physical and chemical atmospheric, land and ocean variables with remote sensing requires an understanding of Earth’s ecosystems. Understanding of systems can be based on expert knowledge, experimental relations or physical relations, and this understanding can be captured in a descriptive model. Models are to understand, detect, predict, and describe interactions within and between ecosystems and the atmosphere across scales that range from local to global.\n\nRemote sensing can be used for parameter input in models, but also for spatial and temporal interpolation or extrapolation. This course provides an introduction to knowledge-driven, data-driven and physical modelling, starting with appropriate model selection given a specific problem or data availability. The course therefore deals with basic concepts and boundary conditions. Much emphasis is on integration of remote sensing observations into models, and selecting optimal object / pixel / time based mapping method for a given problem "@en . "Modeling & Mapping"@en . . "Modeling & Mapping"@en . "Modeling & Mapping"@en . . "37" . "Yes"@en . "No"@en . . "Internship"@en . . "Internship"@en . "Internship"@en . . "26" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . "None"@en . "No"@en . "No"@en . . . . . "4.0" . "2.0" . "The earth gives us a place to live and provides resources that ensure our existence. At the same time, the earth poses challenges in the shape of natural hazards and the results of human overconsumption of resources and resulting pollution. Earth observation from space is the only way to monitor the planet for its overall health and state. In this course, an overview is given of how modern Earth sciences help to understand our planet, how we should use its resources to support our societies and mitigate its hazards, and also what we can do to protect our planet."@en . "[CONCEPT] The Earth is a complex, dynamic system that not only provides us with a habitat but also supplies the resources essential for sustaining modern society and ensuring our survival. The various components of the Earth System, including human activities, interact across space and time, with changes in one part potentially affecting all others in intricate and often unpredictable ways. These interactions determine the overall health of our planet and present challenges such as natural hazards, resource limitations, and constraints on societal growth. Earth observation from space, combined with advanced geoscience techniques, is vital for understanding how the planet works and for monitoring its health and state. This course is designed to equip students with the concepts and tools needed to comprehend the fundamental aspects of the Earth System. It will cover key geodynamic processes, their interactions, and their links to natural hazards and resource availability. Students will also learn modern techniques for imaging the Earth's surface and subsurface, with a focus on their significance in planetary monitoring, hazard assessment and management, and the sustainable exploration and use of natural resources. The students will be able to choose the topics for their assignments from a list of case studies relevant to the course's goals. By the end of the course, students will have a deep understanding of the complex natural processes and feedback mechanisms between the Earth's interior, near-surface dynamics, and activities relevant to society. They will also gain the fundamental technical skills necessary to develop innovative solutions to the societal challenges posed by global environmental change and to capitalize on opportunities in the transition to global sustainability."@en . "Earth Processes & Society"@en . . "Earth Processes & Society"@en . "Earth Processes & Society"@en . . "8" . "5.0" . "140.0" . "10.0" . . . . . "blended" . . . . . . . . . . . . . . . "Foundational courses (basic GIS knowledge and basic statistics)"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . "3.0" . "2.0" . "“Planning for Liveable Cities” critically addresses inequalities within urban areas by analysing concerns about social equity, quality of life, health and well-being and urban competitiveness in light of urban development patterns and strategies. Tensions and trends in planning for these visions and ideals is discussed. Different tools are introduced and applied to analyse these patterns. Students will engage with various scales of analysis, applying geospatial solutions to develop people-centric and digitally-informed strategies that support the transition towards more equitable, healthy, and just urban futures. By capturing and understanding diverse forms of knowledge related to intra-urban variations in quality of life, the curriculum aims to create a deeper understanding of these patterns. This is crucial for targeting deprived areas and formulating effective area-based and people-based policies. "@en . "Planning for Liveable Cities"@en . . "Planning for Liveable Cities"@en . "Planning for Liveable Cities"@en . . "34" . "5.0" . "140.0" . "10.0" . "Yes"@en . "No"@en . . "2.0" . "The premise of the course is motivated by the recent advancements in geoinformation data acquisition and\nstorage and their intended use for evidence-based planning and monitoring. The spatial references of geoinformation data may be attributed to the exact locations of measurements or over a fixed set of contiguous\nregions or lattices. This course seeks to handle the three main classes of spatial data/processes:\ngeostatistical data/spatially continuous process, lattice data/discrete process, and point pattern data/point\nprocess. Such data appear common in diverse application fields like environmental science, agriculture,\nnatural resources, environmental epidemiology, and so on. The aim is to present methods that can be used\nto explore and model such data. Naturally, data vary in space and in time; hence data close to each other\n(either in space or time) are more similar than those farther. Geostatistical modeling based on the\nsemivariance and/or covariances and interpolation (kriging) in space and time will therefore be introduced.\nThe methods will be extended and applied to data aggregated over contagious regions. The uncertainty is\nquantified, and attention will be given to making maps showing the probabilities that thresholds are\nexceeded. Attention is also given to optimal sampling and monitoring. Further, data that arise out of the\noccurrences of events; thus point pattern data will be considered. The significance of exploring the first\nand second-order properties of point patterns in diverse application domains like environmental and\ndisaster (like earthquakes) modeling will be explained and applied. The last focus will be on lattice data; in\nprinciple, this kind of data consists of observed values over a set of fixed contiguous regions. This kind of\ndata is rather easy to acquire and is mostly applied in health studies where data aggregation is a standard\nform of protecting locational privacy."@en . "Geostatistics"@en . . "Geostatistics"@en . "Geostatistics"@en . . "12" . "5.0" . "140.0" . "10.0" . . . . "F2F, blended" . . . . . . . . . . . . . . "Intro to ULF (q1)"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . "2.0" . "2.0" . "Land Administration encompasses four fields i.e. land tenure, land use, land value and land development. These four fields are also referred to as land management practices. This course focuses on the land management practices in the context of the policy frameworks and sustainable development. The land management paradigm is used a guiding framework. The land management paradigm stresses the relationship between land policy and the four land administration functions i.e. land tenure, land value, land use and land development – and the wider societal goals. As such, legal frameworks, institutions, processes, interventions, successes and challenges are discussed in the context of social, economic, environmental pillars of development. Further, how these land management practices also link with emerging issues such as climate change are also discussed. The course therefore addresses both conventional and innovative ways of land management, promoting a paradigm shift towards responsible land administration. The course relates state-of-the-art scientific knowledge to students' experiences, perceptions and country context. "@en . "Responsible Land Administration"@en . . "Responsible Land Administration"@en . "Responsible Land Administration"@en . . "16" . "2.5" . "70.0" . "5.0" . . . . . . "blended" . . . . . . . . . . . . . . . . "Foundation courses M-Geo"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . "4.0" . "2.0" . "The concept of (public) participation in geospatial research has a long tradition. However, the adoption of Web 2.0 technologies facilitates the generation and sharing of and collaboration on digital content with a geospatial component, and has therefore expanded possibilities and practice. This course gives an overview of its history and new developments, on examples of successful and unsuccessful projects to identify criteria for sustainable crowdsourcing or volunteering, including issues of privacy and ethical research. It is particularly relevant for eliciting and arguing the needs, interests, and positions of any stakeholder that incorporates or directly works with the public. A main focus lies on the technologies that enable new forms of participatory sensing, and techniques to assess and improve the quality of such data. "@en . "Volunteered Geographic Information and Geo Citizen Science"@en . . "Volunteered Geographic Information and Geo Citizen Science"@en . "Volunteered Geographic Information and Geo Citizen Science"@en . . "19" . "Yes"@en . "No"@en . . "3D Modelling for city digital twins"@en . . "3D Modelling for city digital twins"@en . "3D Modelling for city digital twins"@en . . "24" . "Yes"@en . "No"@en . . "Communication and outreach"@en . . "Communication and outreach"@en . "Communication and outreach"@en . . "53" . "Yes"@en . "No"@en . . "Review and publication process"@en . . "Review and publication process"@en . "Review and publication process"@en . . "13" . "5.0" . "140.0" . "10.0" . . . . "offline/online, hybrid" . . . . . . . . . . . . "Basic GIS, basic notions on concpetual modeling"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . "7?" . "3.0" . "Urban & Land Geo-data Acquisition & Dissemination"@en . . "Urban & Land Geo-data Acquisition & Dissemination"@en . "Urban & Land Geo-data Acquisition & Dissemination"@en . . "20" . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . "Academic Research Skills"@en . . "Academic Research Skills"@en . "Academic Research Skills"@en . . "3" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . "Q2 (QRS) and preferably Q3 - Modelling and Mapping / open for second year as elective"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . . "4.0" . "3.0" . "[CONCEPT] The earth surface is a dynamic environment that constantly undergoes change. Various process interact at various time scales, ranging from minutes in atmospheric processes to days in land processes and even millions of years in geological processes. Monitoring of natural resources therefore deals with monitoring of a changing earth surface cover. Even when observing geological processes, the observational environment still changes by the minute. \n\nIn this course, remote sensing is applied for monitoring changes in land cover and land use, covering both system drivers (e.g., changes in land use) and response variables. Attention is given to linking the physical world with ethical and social considerations, environment and social aspects of technology, consulting different stakeholders in the management of the resources. "@en . "Impact monitoring and management"@en . . "Impact monitoring and management"@en . "Impact monitoring and management"@en . . "57" . "5.0" . "140.0" . "10.0" . "All students should have basic knowledge of remote sensing"@en . "Yes"@en . "No"@en . . "2.0" . "The use of Unmanned Aerial Vehicles - UAVs (or drones) has surged in the last two decades, leading to\nremarkable changes in several remote sensing applications. However, the development of best practices\nfor high-quality UAV mapping is often overlooked representing a drawback for their adoption in different\ndomains. UAV solutions then require an interdisciplinary approach, integrating different expertise and\ncombining several hardware and software components on the same platform. This course aims to deliver\nboth theoretical and hands-on knowledge to acquire, process and interpret UAV data. The course\naddresses three specific and alternative application domains: precision agriculture, water management\nand scene understanding. The basics of UAV mapping using visible, multispectral and thermal images will\nbe given in the first module of the course. After this first module, the students will be asked to select one of\nthe different application domains."@en . "UAV for Earth Observation"@en . . "UAV for Earth Observation"@en . "UAV for Earth Observation"@en . . "50" . "5.0" . "140.0" . "10.0" . "foundation course GIS and EO foundation | thematic course Quantitative Remote Sensing"@en . "Yes"@en . "No"@en . . "3.0" . "2.0" . "This course deals with the retrieval of quantitative information about vegetation canopies from remote sensing data. In particular, the focus will be on vegetation physiological parameters, namely leaf area index and phenology and how they can be estimated from remote sensing data. \n\nDefinitions and details about these parameters, how they are measured in the field, and how they are estimated using various remote sensing data will be provided during the course.\n \nThe course has a remote sensing focus to model vegetation phenology and biophysical parameters like leaf area index. In addition to interactive lectures, students will be familiarised with lab and field measurements of these parameters and in the practical exercises, they will use multispectral, hyperspectral, Radar and LiDAR data to model and estimate these parameters. \n\nIn the first part, the course deals with the topic of biophysical parameter measurements in the lab/field and their estimations using remote sensing, various statistical approaches and inversion of radiative transfer models. \n\nThe second part will address the topic of phenology and the calculation of phenological metrics by analysing time-series remote sensing data. \n\nDuring a field visit, students will collect data and practice what they have learned earlier in the course. In the last two weeks of the course, students will work on their final assignment and will present it on the last day of the course. "@en . "Quantitative Remote Sensing of vegetation parameters"@en . . "Quantitative Remote Sensing of vegetation parameters"@en . "Quantitative Remote Sensing of vegetation parameters"@en . . "51" . "5.0" . "140.0" . "10.0" . "Yes"@en . "No"@en . . "3.0" . "Radar Remote Sensing is different from optical Remote Sensing and offers unique opportunities in\nobserving and monitoring the Earth surface. This course provides an overview of technology and\napplications related to radar remote sensing. Specifically, Synthetic Aperture Radar (SAR) and advanced\nmethods building on SAR are considered: InSAR (Interferometric Synthetic Aperture Radar), DInSAR\n(Differential InSAR), Time Series InSAR, PolSAR (Polarimetric SAR) and PolInSAR. The students will\nlearn how to choose, handle and pre-process the SAR images and apply advanced methods for\ninformation extraction from these images. Various examples of applications (such as land use land cover\nclassification and land subsidence) will be provided. The quality of obtained results will be discussed in\nrelation to the type of SAR data and choices made during the analysis. The course offers an opportunity to\nspecialise in one of the advanced SAR methods during a practical project."@en . "Radar Remote Sensing"@en . . "Radar Remote Sensing"@en . "Radar Remote Sensing"@en . . "28" . "5.0" . "140.0" . "10.0" . "Basic knowledge on remote sensing. "@en . "Yes"@en . "No"@en . . "3.0" . "Plants play a crucial role in the history of the Earth. They have accelerated the water cycle, and have made soil formation possible, and provide Oxygen through photosynthesis. They are also the primary sink of carbon dioxide, and they are our food. \n\nOngoing changes in climate affect the functioning of plants, but also vice versa: Land cover changes affect the surface properties of the Earth which in turn affect the climate. For sustainable land cover, ecology and food production, we must be able to quantify the role of plants in the climate on Earth. \n\nThis course offers tools to quantify processes in terrestrial vegetation using contemporary remote sensing signals (reflectance, chlorophyll fluorescence, and thermal remote sensing) in combination with in situ data. There is attention for natural ecosystems as well as crops. "@en . "EO for modelling of primary productivity and plant growth"@en . . "EO for modelling of primary productivity and plant growth"@en . "EO for modelling of primary productivity and plant growth"@en . . "36" . "Yes"@en . "No"@en . . "Geoweb Apps and Services & SDI"@en . . "Geoweb Apps and Services & SDI"@en . "Geoweb Apps and Services & SDI"@en . . "23" . "5.0" . "140.0" . "10.0" . "The knowledge gained during the Scientific Geocomputing course is advantageous but not strictly\nnecessary to follow this course. Some self-study material will be provided through Canvas for students that\ndo not follow the Geoinformatics specialisation. Practicals on best practices in developing research code\nin Python will be performed at the beginning of the course to improve the necessary skills."@en . "Yes"@en . "No"@en . . "2.0" . "Thanks to the digital, mobile and sensor revolutions, massive amounts of data are becoming available at\nunprecedented spatial, temporal, and thematic scales. This leads to the practical problem of transforming\nbig geodatasets into actionable information that can support a variety of decision-making processes. In\nthis respect, scalable geodata science workflows are not only key to process big geospatial datasets, but\nalso to share the obtained information and knowledge and to ensure the reproducibility of the results.\nTo handle and analyse massive amounts of potentially heterogeneous spatio-temporal data, GIS specialists\nand researchers need to 1) understand the particular characteristics of big geodata, 2) learn to work with\nscalable data management and processing systems, and 3) develop distributed but robust data mining and\nmachine learning workflows. This course aims to provide the necessary know-how by presenting theories,\nmethods, and techniques to build scalable solutions for handling and analysing big geodata, and develop\nthe necessary skills through hands-on practical and code-along sessions."@en . "Big Geo Data"@en . . "Big Geo Data"@en . "Big Geo Data"@en . . "46" . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . "MSc research proposal writing"@en . . "MSc research proposal writing"@en . "MSc research proposal writing"@en . . "32" . "Yes"@en . "No"@en . . "GeoHealth"@en . . "GeoHealth"@en . "GeoHealth"@en . . "35" . "5.0" . "140.0" . "10.0" . "no formal ones"@en . "Yes"@en . "No"@en . . "2.0" . "INTRODUCTION (CONCEPT BASED ON EXISTING ELECTIVE)\nThis course Geodata Visualization covers aspects of geovisual analytics, in particular, with respect to time\nseries of movement data of people, animals, and goods. The objective of this course is to learn how to\nprepare and integrate, transform, and visually analyse the data to reveal spatio-temporal patterns and\ntrends. Participants will, based on the methods introduced, develop visual environments for answering\nquestions related to a real-world scenario. These visual environments will combine interactive and\ndynamic map and diagram displays with a focus on user-centred design."@en . "Geovisualisation and A/VR"@en . . "Geovisualisation and A/VR"@en . "Geovisualisation and A/VR"@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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Spatial Data Science"@en . . . . . . . . . . . . . . . . . . . . . . . . . "Urban & Land Futures"@en . . . . . . . . . . . . . . . . . . . . . . . . "GeoAI"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Resources Security"@en . . . . . . . . . . . . . . . . . . . . . "Disaster Resilience"@en . . . "Programme" . . .