. . "201900065" . "EOS_0003" . "5"^^ . "140"^^ . "10"^^ . "2022-11-10T23:00:00Z"^^ . "f2f" . "Image analysis requires theoretical concepts and practical skills. Lectures will be used to introduce the topics, followed by reading textbook material. Research articles will also be recommended for those students who are interested in learning more about a specific concept, method, algorithm etc. Practical classes will consist of a mixture of demos, individual work following written instructions, and presentations of the outcomes during feedback sessions. In the practical classes, students will work with existing program codes and modify them (to a limited degree). In this way, the students can get insight into the intermediate stages of the image analysis algorithms and make decisions on the outcomes. Furthermore, a reflection on theoretical concepts will be made. In this way, a solid integration of theory and practice will be achieved."@en . . . . . "Programming skills \n\nImage analysis knowledge ,All students in Geoinformatics specialization are accepted. Students following other specializations should have background in programming and image analysis."@en . . . . . . . . . "4"^^ . "1" . "1A " . . . . "2022-09-04T22:00:00Z"^^ . "In this course, the students will be introduced to advanced image analysis methods dedicated to enriching their geo-information problem-solving abilities. Image processing and analysis methods treated in previous courses, such as conventional hard pixel-based classification, do not take into account spatial correlations in images and, therefore, do not completely exploit the information contained in images. In this course, we aim to introduce more specialized image analysis methods. In particular, Support Vector Machine and Random Forest will be taught for multisource classification at the pixel level. Convolutional Neural Networks (CNNs) and Fully Convolutional Neural Networks (FCN) will be introduced for contextual classification. Advantages and challenges related to multi-temporal image analysis will also be discussed. The methods introduced in this course will be applied to real case studies. "@en . "Advanced Image Analysis"@en . . "Advanced Image Analysis"@en . "Advanced Image Analysis"@en . . . "Learning outcome"@en . . "Apply advanced image analysis methods to classify both single-date and multi-temporal images in support of addressing environmental and societal problems"@en . "Apply advanced image analysis methods to classify both single-date and multi-temporal images in support of addressing environmental and societal problems"@en .