. "Image Processing And Analysis"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Earth observation applications and services"@en . . "7.5" . "Not provided" . . "Hybrid"@en . "TRUE" . . "Big data - earth observation"@en . . "7.5" . "Not provided" . . "Hybrid"@en . "TRUE" . . "Space Image processing"@en . . "7.5" . "Not provided" . . "Hybrid"@en . "TRUE" . . "Semantic technologies for earth observation"@en . . "7.5" . "Ontologies for Describing ΕΟ Products, Linked Data Technologies for ΕΟ data" . . "Hybrid"@en . "FALSE" . . "Earth observation big data and analytics"@en . . "7.5" . "The main objective of the course is to introduce basic concepts and methods for the collection, management, analysis, visualization and dissemination of large-scale land observation data and geospatial products. The course is addressed to postgraduate students of NTUA's MSc courses who have already attended the compulsory courses of the 1st semester and have basic skills in programming languages such as Python, C, C++. Current scientific and technological challenges and solutions for harmonization, fusion and web-based processing of heterogeneous data and production of geospatial products will be described in detail. Upon completion of the course, the student will be able to implement geospatial databases, web-based applications for data search and visualization and geospatial products; design and implement individual automation in data and time series analysis; implement and integrate machine learning methods for information extraction; for applications such as precision agriculture, water quality assessment, automatic detection of changes in urban, natural and marine environments. Course Material\nData collection and automation of geospatial database import and update processes.\nFormats and representations of spectral spatio-temporal data and their characteristics.\nSystems and architectures for storage, management, analysis and delivery of large geospatial data and products in cloud computing systems.\nData visualisation and dimensionality reduction strategies.\nStatistical processing and analysis for data harmonization and merging.\nWeb-based processing and high-performance computing systems for land observation data.\nData and time series analysis for change, object and feature detection.\nBig data analysis using machine learning techniques with applications in precision agriculture, water quality assessment, automatic detection of changes in urban, natural and marine environments." . . "Presential"@en . "FALSE" . . "Computer vision & Image analysis"@en . . "5" . "The course outline is as follows:Introduction, review of mathematical toolsFeature extraction and matchingImage/object classification and scene understandingDeep Learning: from basics to applications Multi-view imagingMotion estimation and tracking3D VisionLabs on earth observation Labs on spacecraft pose estimation\n\nOutcome:\r\nThe course outline is as follows:Introduction, review of mathematical toolsFeature extraction and matchingImage/object classification and scene understandingDeep Learning: from basics to applications Multi-view imagingMotion estimation and tracking3D VisionLabs on earth observation Labs on spacecraft pose estimation" . . "Presential"@en . "TRUE" . . "Earth observation technologies"@en . . "6" . "The goal of this module is to familiarize students with the process of specifying and designing an observational\nmission/campaign (e.g., a new satellite mission or a ground measurement campaign). This process includes the interpretation and\nanalysis of user needs and their translation to observational requirements, the high-level design of possible technical solutions,\nand the evaluation of the expected observational performance with respect to user requirements.\nAfter completing this module, students will be able to:\n-Formulate user requirements that can be assessed based on parameter estimation\n-Evaluate the principles and limitations of generic classes of observation techniques, observation platforms, and data processing\ntechniques \n-Analyse third-party user requirements and translate these into system requirements of an observational system (mathematical\nand physical) \n-Design an observational mission to gather the requested observations \n-Analyse results from an observation mission by estimating the parameters of interest, including quality assessment \n-Reflect on the analysis results in relation to third-party stakeholders \n-Contribute to effective group work and communicate orally and in written form on the project results at an academic level" . . "Presential"@en . "TRUE" . . "Earth observation"@en . . "15" . "During the fieldwork, students will work in teams and receive the terms of reference as defined by a (virtual) client, with the aim\nto analyse local to regional deformations and/or mass displacements due to various natural and/or human-induced causes. They\nneed to design a fieldwork campaign accordingly; the design will be reviewed during the project planning review. In the second\nphase, students collect the data in the field and process historical and newly acquired data. Thereafter they will analyse and\ninterpret the data, present their findings and provide recommendations for future work.\nStudy Goals After completing the module students will be able to:\nGeneral:\n1. Identify open issues in the processing and/or interpretation of Earth system data records based on the outcomes of the lab\nproject and design a development roadmap to address them.\n2. Present analyses, interpretations and conclusions, as well as ethical implications, of the Lab and Fieldwork projects in a clear\nand convincing manner, both orally and written.\nTheory/Lab (module specific):\n3. Assess the value and limitations of Earth observation data in addressing a societal challenge related to geohazards or climate\nimpacts.\n4. Select Earth observation data suitable for the study of a phenomenon of interest.\n5. Design, implement and validate a workflow to derive/extract geophysical parameters from Earth observation data.\n6. Quantify and assess the input data uncertainties and the quality of the results.\n7. Characterize temporal and spatial variations of geophysical parameters using Earth observation data.\n8. Analyse, visualize and interpret findings in a clear and convincing manner.\nFieldwork:\n9. Plan and design a field campaign that is appropriate for the physical process to be measured.\n10. Collect data in the field using different measurement techniques.\n11. Explain and quantify the error sources associated with the field measurements.\n12. Process and analyse the data collected in the field to give meaningful constraints on the physical process.\n13. Effectively communicate with peers, assessors and clients.\n14. Contribute to a project as a team player and to the overall project management." . . "Presential"@en . "TRUE" . . "Introduction to digital Image processing"@en . . "4" . "Learning outcomes\nUnderstand the meaning of signals and image\nUnderstand image formation\nUnderstanding of digitalisation of analogue image\nLearn the image processing algorithms for enhancement of an image\nAcquire an appreciation for the image processing issues and techniques and be able to apply these techniques to real world problems in natural science\nBe able to conduct independent study and analysis of image processing problems and techniques\nBrief description of content\nImage formation\nImage acquisition\nDigitalization\nImage manipulation\nIllumination enhancement\nTransformation\nFrequency domain image processing\nDenosing\nColour processing\nObject recognition" . . "Presential"@en . "TRUE" . . "earth observations"@en . . "4" . "no data" . . "Presential"@en . "FALSE" . . "Earth observation and geoinformation science"@en . . "no data" . "N.A." . . "Presential"@en . "TRUE" . . "Image processing"@en . . "no data" . "no data" . . "Presential"@en . "FALSE" . . "Earth observation"@en . . "no data" . "no data" . . "no data"@en . "TRUE" . . "Image analysis"@en . . "3" . "Selected theoretical and practical problems related to \r\nthe analysis and digital processing of photogrammetric \r\nand remote sensing pan-chromatic and multispectral \r\nimagery obtained from the aviation and satellite alti\u0002tudes. Selection of satellite imagery and methods of its \r\nprocessing as well as the use of specialist software for \r\nprofessional digital processing." . . "Presential"@en . "TRUE" . . "Advanced Image analysis"@en . . "5" . "To give knowledge of advanced methods and models for analyzing image data, and give competence in applying these techniques in different applications. The course attempts to make the participants recognize that the use of appropriate models can extract useful knowledge from image data - knowledge that is not directly accessible." . . "Presential"@en . "FALSE" . . "Image analysis with microcomputer"@en . . "10" . "The course's primary focus is on hardware/software interaction and the development and application of electro-optical systems. Emphasis is on optimal hardware performance, i.e. optics, camera, acquisition HW, in application in surveillance, navigation, medicine and industrial control." . . "Presential"@en . "FALSE" . . "Earth observations for monitoring changes (eo4change)"@en . . "5" . "The Earth is changing, and these changes can clearly be seen from space. Here, we will introduce some of the different satellite-based Earth observation (EO) datasets available, emphasizing real-life use examples. After the introduction, the students (in groups) will work more in-depth with a specific dataset to observe the phenomena they find most interesting (e.g., deforestation, floods, drought, ice thickness). Here, we need to identify relevant satellite missions and spatio-temporal data requirements before diving into the method development, implementation and analysis. Examples of such projects could be, e.g., the use of NASA's ICESat-2 green-laser mission to determine Amazon deforestation, monitor drought across continents with the EU Sentinel missions, or use Sentinel-2 to see the biological activity in the world's oceans. These are just some examples of the data we aim to have the students be able to grasp and analyze. Through this course, we aim for the students to be able to navigate the growing stream of EO data freely available from different agencies and make use of them for society." . . "Presential"@en . "FALSE" . . "Earth observation of water resources"@en . . "7" . "In the first week of this course we introduce the geospatial problem solving approach. For this we consider the differences between uses and users of geo-information for problem solving, and the needs for answering geospatial questions. Furthermore, we discuss the influence of societal differences in selecting approaches and priorities when managing natural resources. We will also discuss the role of geo-information in the context of the Sustainable Development Goals and other global challenges. In the last two weeks of the course you will carry out a project assignment, in which you apply elements of the geospatial problem solving approach to produce geo-information relevant for a specific geospatial problem issue." . . "Presential"@en . "TRUE" . . "Earth observation for natural resources management"@en . . "7" . "no data" . . "Presential"@en . "TRUE" . . "Image analysis"@en . . "7" . "no data" . . "Presential"@en . "TRUE" . . "Earth observation for wetland monitoring and management"@en . . "5" . "no data" . . "Presential"@en . "FALSE" . . "Advanced Image analysis"@en . . "5" . "no data" . . "Presential"@en . "FALSE" . . "Environmental monitoring with satellite Image time series"@en . . "5" . "no data" . . "Presential"@en . "FALSE" . . "Image processing"@en . . "3" . "no data" . . "Presential"@en . "TRUE" . . "Image processing"@en . . "3" . "no data" . . "Presential"@en . "TRUE" . . "Image processing"@en . . "10" . "Not found" . . "Presential"@en . "TRUE" . . "Applications in earth observation"@en . . "20" . "This module is designed to give you: \n \n a knowledge of modern EO satellites, software and methodologies; \n the capability to assess when a problem can be tackled with EO and when this is not possible; \n the ability to design a new EO service/solution that could be used to tackle a problem. On successful completion of the module, you should be able to:\n \n understand and explain to others the working principles of Earth Observation satellites and methodologies;\n employ state of the art software to process EO data to make measurements and solve real problems;\n evaluate when a problem can be tackled using EO data;\n design an EO procedure exploiting Copernicus data to tackle specific problems." . . "Presential"@en . "TRUE" . . "Image processing and machine learning"@en . . "14" . "sharing basic knowledge of Image recognition, image analysis and application of methods of machine learning" . . "Presential"@en . "FALSE" . . "Image processing and analysis"@en . . "6.0" . "### Working language\n\nPortuguês - Suitable for English-speaking students\n\n### Goals\n\nThe objective of this curricular unit is to present the main concepts and techniques of Digital Image Processing (PDI) with emphasis on images acquired by remote sensing sensors.\n\n### Learning outcomes and skills\n\nIt is intended that students:\n\n\\- Know and understand the main concepts and methods used in remote sensing image processing.\n\n\\- Be able to select and use the appropriate PDI tools to extract relevant information from remote sensing images.\n\n\\- Be able to apply the knowledge acquired in the effective analysis of simulated and experimental data, using advanced computational means.\n\n### Working mode\n\nIn person\n\n### Program\n\n1\\. Basic concepts of digital image processing.\ntwo\\. Spot operations / Image calibration.\n3\\. Spatial Filters / Noise Reduction.\n4\\. Color representation models.\n5\\. Image segmentation.\n6\\. Morphological Operations.\n7\\. Geometric Corrections / Image referencing.\n8\\. Multi-Spectral Imaging / Principal Components.\n9\\. Classification of multi-spectral images.\n10\\. Clustering / Unsupervised sorting.\n11\\. Operations in frequency space.\n\n### Mandatory Bibliography\n\nRichards J.A. Jia X.; [Remote Sensing Digital Image Analysis](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000227774 \"Remote Sensing Digital Image Analysis (Opens in a new window)\")\nGonzalez Rafael C.; [Digital image processing](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000260721 \"Digital image processing (Opens in a new window)\"). ISBN: 0-13-008519-7\nRafael C. Gonzalez; [Digital image processing using MATLAB](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000278836 \"Digital image processing using MATLAB (Opens in a new window)\"). ISBN: 0-13-008519-7\n\n### Complementary Bibliography\n\nSzeliski, R.; Computer Vision: Algorithms and Applications, Springer, 2010\nSonka Milan; [Image processing, analysis, and machine vision](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000281201 \"Image processing, analysis, and machine vision (Opens in a new window )\"). ISBN: 0-495-08252-X\n\n### Teaching methods and learning activities\n\nThe TP classes are used in part for the presentation of theoretical material, illustrated with varied examples, and there is another part dedicated to the execution of small practical (computational) projects. The “Other” type classes are used to support the realization of practical computational work as well as clarify any doubts that students may have.\n\n### Software\n\nSNAP\nMATLAB\n\n### Type of evaluation\n\nDistributed evaluation with final exam\n\n### Assessment Components\n\nOral test: 20.00%\n\nWritten work: 30.00%\n\nPractical or project work: 50.00%\n\n**Total:**: 100.00\n\n### Occupation Components\n\nSelf-study: 60.00 hours\nFrequency of classes: 42.00 hours\nProject development: 40.00 hours\nWritten work: 20.00 hours\n\n**Total:**: 162.00\n\n### Get Frequency\n\nRealization of Practical Works, with the delivery of the respective reports within the established deadlines, and with a classification of not less than 40% of the corresponding quotation (8 values in the 0-20 scale).\nStudents may have to answer questions related to the practical work carried out, during classes or in an oral exam.\n\n### Final classification calculation formula\n\nThe final classification will be determined based on the performance in practical work (50%) and in an individual mini-project (50%), none of these components being able to be less than 40% of the corresponding quotation (8 values in the 0-20 scale).\n\nMore information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479404" . . "Presential"@en . "TRUE" . . "Applications in metheorology and climage changes"@en . . "3.0" . "Information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479358" . . "Presential"@en . "FALSE" . . "Object-based Image analyis"@en . . "3" . "overall understanding of object-based image analysis as an advanded image understanding strategy\n– applying spatial concepts in image analysis, such as geometrical, form-related, context-related properties of objects\n– handling basic technical principles of image segmentation and object-based classification and validation." . . "Online"@en . "FALSE" . . "Image processing and analysis"@en . . "6.0" . "https://sigarra.up.pt/fcup/en/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=502137" . . "Presential"@en . "FALSE" . . "Signal and Image processing"@en . . "6.0" . "This course offers the student a hands-on introduction into the area of digital signal and image processing. We start with the fundamental concepts and mathematical foundation. This includes a brief review of Fourier analysis, z-transforms and digital filters. Classical filtering from a linear systems perspective is discussed. Next wavelet transforms and principal component analysis are introduced. Wavelets are used to deal with morphological structures in signals. Principal component analysis is used to extract information from high-dimensional datasets. We then discuss Hilbert-Huang Transform to perform detailed time-frequency analysis of signals. Attention is given to a variety of objectives, such as detection, noise removal, compression, prediction, reconstruction and feature extraction. We discuss a few cases from biomedical engineering, for instance involving ECG and EEG signals. The techniques are explained for both 1D and 2D (images) signal processing. The subject matter is clarified through exercises and examples involving various applications. In the practical classes, students will apply the techniques discussed in the lectures using the software package Matlab.\n\n \n\nPrerequisites\nDesired Prior Knowledge: Linear algebra, Calculus, basic knowledge of Matlab. Some familiarity with linear systems theory and transforms (such as Fourier and Laplace) is helpful.\n\nRecommended reading\nPrincipal Component Analysis, Ian T. Jolliffe, Springer, ISBN13: 978-0387954424.\n\nMore information at: https://curriculum.maastrichtuniversity.nl/meta/466801/signal-and-image-processing" . . "Presential"@en . "FALSE" . . "Deep learning for Image & video processing"@en . . "6.0" . "Applications of image and video processing will be presented, and connections to basic algorithms will be demonstrated. We will examine some of the most popular and widespread applications, namely security, surveillance, medical, traffic monitoring, astronomy, farming, culture. The methods used in these applications will be analysed in class and common characteristics between them will be explained. Students will be able to suggest further applications of interest to them and bring relevant literature to the class.\n\nPrerequisites\nDesired prior knowledge: Image and Video Processing, Calculus, Linear Algebra, Machine Learning.\n\nRecommended reading\nRafael C. Gonzalez and Richard E. Woods, Digital Image Processing (3rd Edition), Prentice Hall.\nA. Bovik (Ed.), The Essential Guide to Video Processing. Academic Press, 2009.\n\nMore information at: https://curriculum.maastrichtuniversity.nl/meta/463079/deep-learning-image-video-processing" . . "Presential"@en . "FALSE" . . "Fundamentals of earth observation"@en . . "9.0" . "The module aims at providing a general background on the remote sensing systems for Earth Observation from airborne, and espe-cially space-borne platforms that operate in different regions of the electromagnetic spectra.\nIt provides the fundamental knowledge about the physical bases for remotely sensing the Earth, and in particular the electromagnetic foundation and models describing the emission, absorption and scattering of the radiation by natural media (atmosphere, sea, land) which are required for data interpretation.\nIt describes, using a system approach, the characteristics of the system to be specified to fulfil the final user requirements in different application domains. It reviews the technical principles of the main sensors operating in different ranges of the electromagnetic spec-trum and illustrates the constraints due to the system (sensor, orbit, etc) in matching the user requirements. It provides an overview of the most important applications and bio-geophysical parameters (of the atmosphere, the ocean and the land) which can be re-trieved in different regions of the electromagnetic spectrum. It reviews the most important techniques for data processing and prod-uct generation and proposes practical exercises using the computer to introduce the main processing steps. Finally, it provides an overview of the main Earth Observation satellite missions and the products they provide to the final user." . . "Presential"@en . "TRUE" . . "Earth observation"@en . . "6.0" . "The module aims to provide basic and broad-spectrum knowledge on remote sensing systems for observing the Earth from aircraft and satellite and on the European Union Copernicus services for monitoring our planet and its environment with the use of satellite data. Copernicus services concern the management of the land and major renewable and non-renewable resources, the marine environment, the atmosphere, and environmental safety in a context of sustainable use of resources and the impact on climate change. The module describes, with a systems approach, the requirements and general characteristics of the system in relation to the final application. It illustrates the physical bases of remote sensing and simple models of electromagnetic interaction with natural means useful for the interpretation of data. It illustrates or recalls the operating principles of the main remote sensing sensors in the different regions of the electromagnetic spectrum. It illustrates the main techniques of remote sensing data processing for the purpose of generating application products, also with the aid of computer exercises. It provides an overview of the information on the terrestrial environment (atmosphere, sea, vegetation, etc.) detectable in the different bands of the electromagnetic spectrum. It describes the main Earth Observation space missions, and the most significant characteristics of the products supplied to end users." . . "Presential"@en . "TRUE" . . "Earth observation"@en . . "5.0" . "This course is taught at the UGent. \n\nKeywords: \nEarth observation, remote sensing \n\nPosition of the course\n\nAcquiring an overview of and understanding of different possibilities of application of earth observation, both for the observation of physical properties of the Earth as for socio-economic activities and how the interaction environment-men on Earth can be observed from space. This introduction will be given by guest lecturers directly involved in the use and development of Earth observation (techniques) in a wide variety of disciplines.\n\nContents\n\nIn a half dozen guest lectures of half a day the use of earth observation techniques is explained in several themes, such as: \n* evolution of soil degradation; \n* urban development; \n* protection of (world) heritage; \n* floods: impact study; \n* observation of forest fires; \n* impact of changing climate; \n* evolution of deltas; \n* study of land slides; \n* … \n\nInitial competences\nNone \n\nFinal competences\nUnderstanding the possibilities of earth observation. \n\nMore information at: https://studiekiezer.ugent.be/studiefiche/en/122006486/2023" . . "Presential"@en . "TRUE" . . "The moving Image"@en . . "20.0" . "https://portal.stir.ac.uk/calendar/calendar.jsp?modCode=FMSU9A2&_gl=1*ctz2u9*_ga*MTY1OTcwNzEyMS4xNjkyMDM2NjY3*_ga_ENJQ0W7S1M*MTY5MjAzNjY2Ny4xLjEuMTY5MjAzOTA0NS4wLjAuMA.." . . "Presential"@en . "FALSE" . . "Earth observation (envu9eo)"@en . . "20.0" . "https://portal.stir.ac.uk/calendar/calendar.jsp?modCode=ENVU9EO&_gl=1*165oolf*_ga*MTY1OTcwNzEyMS4xNjkyMDM2NjY3*_ga_ENJQ0W7S1M*MTY5MjAzNjY2Ny4xLjEuMTY5MjAzOTk5Mi4wLjAuMA.." . . "Presential"@en . "FALSE" . . "Earth observation (envu9eo)"@en . . "20.0" . "https://portal.stir.ac.uk/calendar/calendar.jsp?modCode=ENVU9EO&_gl=1*g5o1ly*_ga*MTY1OTcwNzEyMS4xNjkyMDM2NjY3*_ga_ENJQ0W7S1M*MTY5MjAzNjY2Ny4xLjEuMTY5MjA0MDA2Ni4wLjAuMA.." . . "Presential"@en . "FALSE" . . "Earth observation for environmental monitoring"@en . . "6" . "Principles of earth observation for environmental monitoring, satellite imagining sensors, remote sensing, image processing and the trends for environmental monitoring. Upon completion of this course, it is expected that the learner will be able to: (1) demonstrate the ability to complete an independent, in-depth, thorough and systematic study related to: Climate Change Monitoring, Water Resource Management, Disaster Risk Reduction, Access to Energy and Big EO Data Analytics, (2) interpret and evaluate conclusions from data analysis, and develop results validated through a sound research methodology, (3) prioritise and critically assess earth observation sensors and space-based solutions for environmental applications." . . "Presential"@en . "TRUE" . . "Special topics in earth observation"@en . . "6" . "Advanced remote sensing technologies and their use in natural and build environment. Learn, critically assess, implement and evaluate a variety of image processing algorithms for satellite datasets. Upon completion of this course, it is expected that the learner will be able to show expertise in advanced optical satellite remote sensing applications." . . "Presential"@en . "FALSE" .