. . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . "Basic GIS and Remote Sensing skills"@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 . . "2.5" . "70.0" . "5.0" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "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 Geoinformation Engineering"@en . . "3D Geoinformation Engineering"@en . "3D Geoinformation Engineering"@en . . "5.0" . "140.0" . "10.0" . "Basic programming"@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 Geocomputing"@en . . "Scientific Geocomputing"@en . "Scientific Geocomputing"@en . . "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" . . . . . . "blended" . . . . . . . . . . . . . . . . "Basic statistical analyses, Spatial statistics"@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 . . "5.0" . "140.0" . "10.0" . . . . . . "F2F" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "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 . . "2.5" . "70.0" . "5.0" . . . . . . . "blended" . . . . . . . "operational GIS skills"@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 . . "2.5" . "70.0" . "5.0" . . . . . "blended" . . . . . . . . . . . . . . . "None"@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 . "Land and Urban Futures"@en . . "Land and Urban Futures"@en . "Land and Urban Futures"@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" . . . . . "f2f / blended / online " . . . . . . . . . . . . . . . "Foundation Courses, introduction to Urban Land Futures, Data sharing, Data engineering"@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 . "Land Information in Practice"@en . . "Land Information in Practice"@en . "Land Information in Practice"@en . . "5.0" . "140.0" . "10.0" . . . . . . "F2F" . . . . . . . . . . . . . . . . "Machine Learning for Geosciences or equivalent"@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 . . "5.0" . "140.0" . "10.0" . . . . . . . . "F2F" . . . . . . . . . . . . . . . . . . "Statistics, calculus, linear algebra, analytics geometry, programming (Python)"@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 . . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . "MGEO - foundation course"@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 (LiLa and GSL)"@en . . "Lab & Field Work Skills (LiLa and GSL)"@en . "Lab & Field Work Skills (LiLa and GSL)"@en . . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . "Foundation, CORE Book"@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 . . "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" . . . . . . . . . . . . . "CORE MODULE"@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 . . "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 . . "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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "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 . . "2.5" . "70.0" . "5.0" . "Foundation, CORE Book"@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 . . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . "previous ULF courses or similar knowledge and experiences"@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 . . "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" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "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 . . "5.0" . "140.0" . "10.0" . . . . "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 . "Earth processes"@en . . "Earth processes"@en . "Earth processes"@en . . "5.0" . "140.0" . "10.0" . . . . . "blended" . . . . . . . . . . . . . . . "Foundational courses (basic GIS knowledge and basic statistics)"@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 . . "5.0" . "140.0" . "10.0" . . . . "F2F, blended" . . . . . . . . . . . . . . "Intro to ULF (q1)"@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 . . "2.5" . "70.0" . "5.0" . . . . . . "blended" . . . . . . . . . . . . . . . . "Foundation courses M-Geo"@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 . . "5.0" . "140.0" . "10.0" . . . . "offline/online, hybrid" . . . . . . . . . . . . "Basic GIS, basic notions on concpetual modeling"@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 . . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . "Q2 (QRS) and preferably Q3 - Modelling and Mapping / open for second year as elective"@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 . . "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 . . . . . . . . . . . . . . . . . . . . . . "Urban Land & Futures"@en . . . . . . . . . . . . . . . . . . . . . "GeoAI"@en . . . . . . . . . . . . . . . . . . . . . . . . . "Resources Security"@en . . . . . . . . . . . . . . . . "Disaster Resilience"@en . . . "Programme" .