. . . "Apply orthorectification to derive orthophoto."@en . . "Apply orthorectification to derive orthophoto."@en . . . "Evaluate attribute and scale uncertainty and relate it to the quality of derived orthophotos, accuracy of resulting image classification and matching.\n"@en . . "Evaluate attribute and scale uncertainty and relate it to the quality of derived orthophotos, accuracy of resulting image classification and matching.\n"@en . . . "Develop an image processing chain using non-linear filters and mathematical morphology operations for automatic information extraction from images in context of a given problem."@en . . "Develop an image processing chain using non-linear filters and mathematical morphology operations for automatic information extraction from images in context of a given problem."@en . . . "Choose and apply a segmentation method to a given image and describe the uncertainty of the obtained result."@en . . "Choose and apply a segmentation method to a given image and describe the uncertainty of the obtained result."@en . . . "Make informed decisions on appropriate image matching method for a given type of data and problem."@en . . "Make informed decisions on appropriate image matching method for a given type of data and problem."@en . . . "Make informed decisions on the best classification method for a given set of images and a specific problem."@en . . "Make informed decisions on the best classification method for a given set of images and a specific problem."@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "The Master’s Programme Geo-Information Science and Earth Observation (M-GEO) is a two-year academic curriculum at MSc level, taught fully in English, dedicated to understanding the earth’s systems from a geographic and spatial perspective. The field of Geo-information Science and Earth Observation has, in recent years, witnessed fast scientific and technological developments. As a result, geographic information has become a vital asset to society and part of our daily life. The ubiquitous production and availability of spatial data require cloud computing and new technology to turn the increasing volume of ‘big data’ to good use. The growing range of global challenges, from climate change and resource depletion to environmental pollution and pandemic diseases, that our society and in particular the more vulnerable populations on our planet are facing, increases the demand for academic professionals who have the ability, attitudes and skills to design solutions that are sustainable, transdisciplinary and innovative with positive societal impacts. Our education focuses on addressing these global problems by means of advanced geo-information and earth observation applications."@en . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . . . . . . . . . . . . . "Geoinformatics"@en . "GFM"@en . . . "Course"@en . "201800301" . "GFM_004" . "7"^^ . "196"^^ . "10"^^ . "2023-07-06T22:00:00Z"^^ . "f2f" . "Image analysis requires a mixture of theoretical concepts and practical skills. The subjects will be introduced in lectures and applied in practical classes. As a preparation for lectures, reading textbook material will be recommended on some subjects. In addition, on some other subjects reading research articles will be recommended after the lecture to go deeper into the subject.\nPractical classes will consist of a mixture of a demo by an instructor, individual work following written instructions and summarizing the outcome of the exercise in a class. In practical class students are supposed to work with existing programming codes and modify these (to a limited degree). In this way the students can get insight in the intermediate stages of the image analysis algorithms and make decisions on the outcomes. In these summaries reflection on theoretical concepts will be done. In this way a solid integration of theory and practice will be achieved."@en . "Preferably subjects as covered in the courses Scientific Geocomputing, Acquisition and Exploration of Geospatial Data and the course Extraction, Analysis and Dissemination of Geospatial Information ,Knowledge and skills in programming, linear filters, basic image classification, basic photogrammetry.\n\nPreferably subjects as covered in the courses Scientific Geocomputing, Acquisition and Exploration of Geospatial Data and the course Extraction, Analysis and Dissemination of Geospatial Information."@en . "6"^^ . "4" . "2B" . "2023-04-23T22:00:00Z"^^ . "In this course, you will be introduced to more advanced image analysis methods enabling to enrich your geo-information problem solving abilities. Image processing methods treated in previous courses, such as linear filters, feature based DTM production and conventional hard pixel based classification, face limitations making them insufficient for reliable geo-information extraction in automatic settings. Non-linear filters will be introduced for reduction of noise while preserving the boundaries. In addition, interest operators will be introduced to detect stable structures in images that are invariant to scale and rotation transformation. Various methods for dealing with objects in images will be studied: mathematical morphology and segmentation. Fuzzy and sub-pixel classification will be introduced to deal with uncertainty and to increase the information content extracted from the imagery. For multisource classification decision trees will be introduced. To automatically detect corresponding image positions, the image matching techniques will be introduced. In particular, area-based matching and feature-based matching will be investigated in this course."@en . "Image Analysis"@en . "Image Analysis"@en . "Image Ana