. . . "Explain the advantages and disadvantages of the advanced image analysis methods introduced in this course"@en . . "Explain the advantages and disadvantages of the advanced image analysis methods introduced in this course"@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 . . . "Understand and describe the four main steps of the modern IBM process for UAV imagery"@en . . "Understand and describe the four main steps of the modern IBM process for UAV imagery"@en . . . "Critically interpret the classification results obtained using advanced image analysis methods"@en . . "Critically interpret the classification results obtained using advanced image analysis methods"@en . . "https://ltb.itc.utwente.nl/page/792/concept/152727" . . "Random forest"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152762" . . "Change detection"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152853" . . "Machine learning"@en . . . . . . . . . . . . . . "https://ltb.itc.utwente.nl/page/792/concept/152878" . . "Support vector machine"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152880" . . "Multi-temporal analysis"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152902" . . "Deep learning"@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 . . . . "Google Colab"@en . . . "Python"@en . . . "R"@en . . . "Course"@en . "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 Ana