. . . . "ITC Bok"@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 . . "201900071" . "WRS_0002" . "5"^^ . "140"^^ . "10"^^ . "2023-02-02T23:00:00Z"^^ . "f2f" . "online" . "Lectures, practicals (workshops), tutorials, individual assignment and group work and written tests."@en . . . . . . "Knowledge of Programming and skills to work on a server in LINUX environment are beneficial for the learning process ,Successful completion of year 1 M-GEO WREM specialization courses, or equivalent."@en . . . . . . . . "4"^^ . "2" . "1B " . . . "2022-11-13T23:00:00Z"^^ . "Data assimilation is a standard practice in numerical weather prediction (e.g., as implemented in the European Centre for Medium-Range Weather Forecasts, ECMWF), and is increasingly used in many other areas of climate, atmosphere, ocean, land and environment modeling.\n\nData Assimilation is a process in which observations are assimilated into a dynamical numerical model in order to determine as accurately as possible the state of the physical system. This course will introduce the theoretical background, the state-of-the-art methods and practical systems, and examples of data assimilation."@en . "Data Assimilation "@en . . "Data Assimilation "@en . "Data Assimilation "@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 . . "201800303" . "WREM_003" . "7"^^ . "196"^^ . "10"^^ . "2023-04-20T22:00:00Z"^^ . "distance education" . "f2f" . "The course SHADES-OF-BLUE will be offered as part of the M-GEO programme and will therefore be delivered in a hybrid setup (face-to-face and online) in the teaching rooms of the University of Twente. The lectures will be recorded and shared with the students. During the lectures, students are exposed to new concepts followed by hands-on practical exercises. A field excursion is organized to provide the students with practical skills to collect in-situ data for calibration and validation purposes. The students are requested to be physically present during the field excursion to improve their learning gain.\n\nDuring the assignment, the students will be coached while they are working on developing the specific application of the assignment. The students are requested to work in groups and prepare a case study from the selected challenge and provide the details of the application developed as well as the results obtained in a report supported by a poster presentation.\n\n \n\nThe main sub-courses forming this course (namely, Ocean-climate nexus, Coastal systems and sea-level rise, Water pollution and Blue productivity) with their corresponding challenges will also be offered as distance education courses."@en . . . . . . "M-GEO core & preferably WREM specialization track courses from Quartile 2. ,Basic knowledge in remote sensing and spatial data analysis\nBackground in physics, biology, earth sciences and/or applied mathematics\nAffinity of working with EO data and natural resources"@en . . . . . . . . . "4"^^ . "3" . "2A" . . "2023-02-05T23:00:00Z"^^ . "This teaching course SHADES-OF-BLUE aims at providing the students with the competence to use Earth Observation (EO) data and products to leverage the management of coastal and inland aquatic resources and policymaking.\n\nThe main objective is to deepen and broaden the knowledge and practical skills of students in using EO products and applications for the integrated management of aquatic resources in deltas. The course includes technical skills and know-how about EO data, products, and applications and, more importantly, global phenomena related to ocean-land-atmosphere interactions. EO products and applications are fundamental components of the planned course and form the backbone of the teaching from the start to the end. Therefore, the course will not only focus on the more generic building stones of remote sensing of aquatic resources but also on the wider scope of applications that addresses the water-atmosphere-land nexus with a deeper analysis and evaluation phase. During this course, the students will acquire competencies needed to address the national (Dutch Research Agenda, routes nr. 1, 4, 9, 13, 23, and 25) and the international research agenda (UN’s Sustainable Development Goals nr. 6, 13, 14, 15)."@en . "SHADES-OF-BLUE: Earth Observation of coastal and inland waters"@en . . "SHADES-OF-BLUE: Earth Observation of coastal and inland waters"@en . "SHADES-OF-BLUE: Earth Observation of coastal and inland waters"@en . "https://ltb.itc.utwente.nl/page/792/concept/152842" . . "Earth observation