. . . . "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 . . "201900061" . "EOS_0007" . "5"^^ . "140"^^ . "10"^^ . "2022-11-10T23:00:00Z"^^ . "f2f" . "hybrid" . "The course will be composed of lectures (with the use of flipped classrooms, or pre-recoreded videos when necessary), practical, assignments and/or fieldwork for UAV image acquisitions. The student will learn how to correctly process the acquired images receiving both the theoretical and practical knowledge and gaining in self-confidence and independence during the course."@en . . . . . . . "Specialization: GeoinformaticsStream course: Image AnalysisNote that we offer two UAV courses and that students from other specialisations/outside ITC should choose the Earth Observation with UAVs course,Specialization: Geoinformatics\nStream course: Image Analysis\n\n \n\nNote that we offer two UAV courses and that students from other specialisations/outside ITC should choose the Earth Observation with UAVs course in principle.\n\nIn case of any doubt, the students can contact course coordinator for clarification. "@en . . . . . . . . . "4"^^ . "1" . "1A " . . . . "2022-09-04T22:00:00Z"^^ . "Unmanned Aerial Vehicles (UAVs) are becoming a valid alternative to traditional Geomatics acquisition systems, as they close the gap between higher resolution terrestrial images and the lower resolution airborne and satellite data. UAVs can be remotely controlled helicopters, fixed wind airplanes or kites. This course deals with algorithms and techniques for scene information extraction from images. Both geometric (i.e. 3D reconstruction) and semantic (i.e. 2D image understanding) aspects are described in the course.\n\nIn this course the 2D and 3D scene analysis will be explained, with focus on the use of data acquired by UAVs. The course is composed of two main parts. In the first part, the participants will focus on 2D scene analysis (semantic segmentation, object detection and tracking, modern deep learning), while during the second part, the participants will gain hands-on experience on the use of UAVs. The second part of the course will be given together with the course on “Earth Observation with UAVs”.\n\nAt the end of the course the participants will submit the output of an assignment on the dealt topics, the quality of which will contribute to the course mark."@en . "Unmanned Aerial Vehicles for Scene Understanding"@en . . "Scene Understanding with UAV's"@en . "Scene Understanding with UAV's"@en . . . . . . . . . . "Introduction to Deep Learning"@en . "Advanced Machine Learning for Geospatial Sciences: LU2"@en . "https://ltb.itc.utwente.nl/page/792/concept/152729" . . "Object detection"@e