. . . . "ITC Bok"@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" . . . . . . "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" . . . . . . . . . . . . . . "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" . . . . . . . . . . . . . "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 . . "202001457" . "CORE_0002" . "5"^^ . "140"^^ . "3"^^ . "2022-11-10T23:00:00Z"^^ . "f2f" . "The course mainly consists of a mix of lectures, practicals and self-study time. Main topics' concepts and theory are briefly introduced through lectures which are usually scheduled in the morning. The subsequent practicals provide an illustration of the introduced concepts to increase understanding and also allow participants to develop practical skills. They consist of a supervised part, to help participants to start up practical activities and to discuss the intermediate results, and an unsupervised part for self-directed learning and skills development. Topics are usually finalised with plenary wrap-up sessions in the afternoon in which conceptual and practical issues that emerged from the lecture and practical are dealt with. Questions which arise at other later moments can be issued to the online discussion fora. Fellow students are encouraged to help solving the issues with moderation of the responsible lecturer.\n\nOver time the practical instructions become less instructive and more task oriented, thus requiring a more active and self-supporting attitude from participants. This also helps to prepare for the planning and execution of the project assignments that you will carry out later on in the programme at ITC. Most pratical exercises will have a generic nature with specific instructions for QGIS. Alternatives can be available for ArcGIS and ERDAS."@en . . . . . . "Compulsory course for students in the Master of Science degree programme in Geo-Information Science and Earth Observation. The suitability of other candidates will be assessed on an individual basis."@en . . . . . . . . . . "13"^^ . "1" . "1A" . "2022-09-04T22:00:00Z"^^ . "Geo-Information Systems and Science (GIS) and Earth Observation by Remote Sensing (RS) are among the main focus areas of the Faculty ITC. We concentrate on the underlying geospatial concepts that contribute to the development of technological innovations. With the help of GIS and RS we also increase our understanding of aspects of system Earth. GIS and RS help us in making contributions to solutions for global challenges, such as the dealing with effects of climate change and rapid urbanisation, and the need for a more sustainable use of our resources.\n\nThis first quartile (entitled 'GIS and RS for Geospatial Problem Solving') of your study programme at ITC consists of three interrelated courses. In these courses we aim to provide you with a general understanding about GIS and RS principles, and with hands-on experience in using software tools for handling and processing geospatial data. Apart from the geo-technological focus, the courses also challenge you in developing an attitude of using GIS and RS in dealing with geospatial problems and answering geospatial questions related to real world problems and challenges. The three courses will take you through the main stages of a geospatial problem solving cycle: from the identification of a geospatial problem and associated questions, via the acquisition, management and exploration of maps, images and other geospatial data, to the analysis and processing of images and spatial data, and eventually to the generation and communication of geospatial information needed for answering the geospatial questions."@en . "Geospatial data: concepts, acquisition and management"@en . . "Geospatial data: concepts, acquisition and management"@en . "Geospatial data: concepts, acquisition and management"@en . "Core_0002" . . "201800292" . "NRM_003" . "7"^^ . "196"^^ . "10"^^ . "2023-04-20T22:00:00Z"^^ . "f2f" . "online" . "The course takes a student-centered (inquiry-based) approach to teaching and learning. Students assume an active/participatory role in their education, while teachers are facilitators who encourage interaction with new material presented and reflective thinking. The teacher uses class discussions, hands-on practicals and other experiential learning tools to track student comprehension, learning needs and academic progress over a teaching unit. Three summative assessments (written exam + individual assignment + final group project) measure how well the students achieve higher order thinking and learning outcomes."@en . . . . . . "NRM_001 and NRM_002 are not prerequisites. Basic knowledge on and skills in remote sensing and GIS. ,Geo-Information Science and Earth Observation: A Systems-Based Approach\n\nSystems Approach for Management of Natural Resources (NRM specialization 2.1)\n\nFrom Data to Geo-Information for Natural Resources Management (NRM specialization 2.2)\n\nNRM_001 and NRM_002 are preferred. "@en . . . . . . . . . . "9"^^ . "3" . "2A" . . "2023-02-05T23:00:00Z"^^ . "The 21st century has witnessed an increase in the availability of Earth observation (EO) data and their use in addressing critical problems in natural resources management (NRM). The myriad of datasets and stakeholder needs can make the selection of a specific sensor and analytical technique to address a problem a daunting task. At the heart of this dilemma is the scale of observation at which we can effectively address the problem. Biophysical processes, flows or interactions can occur at the plant, canopy or regional scale. Similarly, image-based map products have a specific purpose. For example, food security analysts may want to know the location of crop field boundaries in an agroecosystem, while foresters may want to assess forest stand biomass.\n\nThe guiding principle of this course, therefore, is to use the scale observation together with stakeholder needs to select and apply an appropriate EO dataset and analytical technique to solve problems within the three NRS Forest, Agriculture and Environment in the Spatial Sciences (FORAGES) themes (biodiversity conservation, forest management and food security analysis). In the end, students will be able to design a workflow to address these problems that includes the appropriate selection of EO data and analytical techniques."@en . "Mapping and monitoring for natural resources management"@en . . "Mapping and monitoring for natural resources management"@en . "Mapping & Monitoring for NRM/EO for Natural Resources Management"@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" . . . . . . . . . . . . . . . . "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 . . . . . . . "Explain and compare remote sensing based 3D geoinformation data acquisition and processing "@en . . . . . . . . . . . . . . . . . "Monitoring and visualisation changes in time"@en . "Impact monitoring and management: LU2"@en . . . . . . . . . . . . "Foundations of GIS and Earth Observation (Part 1) (M-T-W)"@en . "GIS & EO foundation: LU1"@en . . . . . . . . . . . . . . . "Principles of EO from space"@en . "Quantitative Remote Sensing 5 EC Resources Security: LU1"@en . "https://ltb.itc.utwente.nl/page/792/concept/152732" . . "Remote sensing"@en .