. "Geographic Information Science"@en . . "Remote Sensing"@en . . "Computer Science"@en . . "English"@en . . "Introduction to geographical information systems and science"@en . . "20" . "This module introduces students to the fundamental concepts, techniques and ideas that shape GI Science and the associated GIS Software. The module will be taught as a mix of lectures and intensive practicals. The lecture series will introduce key concepts and analytical approaches used within GI Science including; the foundations of GIS, spatial data models, data input and output, core spatial modelling and specialist analytical approaches and techniques. The practicals will be based around the core ArcGIS Pro software programme. The module will familiarise students with the software through a series of cumulative practical exercises based on a series of GIS applications from a range of subjects including geography, environmental modelling and planning. Students will also gain experience of manipulating and understanding a range of digital spatial data in the course of this module.\n\nOutcome:\nOn successful completion of the module, students should be able to:\r\nDefine key concepts and analytical approaches used within GI Science.\r\nRecognise the operating environments of GIS packages including ArcGIS and MapInfo.\r\nDemonstrate practical skills in the use of GIS packages.\r\nManipulate and display vector and raster digital data.\r\nManipulate, prepare and structure raw spatial data for use within GIS.\r\nCarry out a range of spatial data analyses in both vector and raster modeling environments." . . "Presential"@en . "TRUE" . . "Aerial surveys and drone operations"@en . . "10" . "This module is taught within three broad areas. The first (i) introduces the key concepts of passive airborne surveys, including image capture methodologies, navigation and sensor technology and photogrammetric principles. A second area (ii) introduces the students to an active airborne survey technique, Light Detection and Ranging (LiDAR) and the potential complementary capabilities of this technology for different environments. The final component (iii) demonstrates the opportunities provided by drones as a new airborne survey platform, encompassing hardware, datasets, flight planning and operational restrictions. The module is a combination of theoretical and practical based sessions using both commercial and open source software.\r\n\r\nLecture Topics include; Applications of Aerial Surveys; Historical Development of Photogrammetry; Global Navigation Satellite Systems and Inertial Measurement Units; Determining Camera Interior and Exterior Orientation; Orthophotography and Image Distortions; Collinearity equations; Aerial triangulation; Bundle block adjustment; Structure from motion; LiDAR survey platforms; Types of laser scanner; LiDAR accuracy and errors; LiDAR processing and filtering; Sensor calibration and performance; Point cloud classification; Generation of DEMs and DSMs; Drone Operations and Restrictions; Planning Aerial Survey Campaigns; Drone hardware and datasets.\n\nOutcome:\nOn successful completion of the module, students should be able to:\r\nDescribe the theoretical principles underpinning the use of photogrammetry for data provision.\r\nIdentify and suggest methods to remove distortions from aerial images.\r\nCritically compare typical outputs of aerial hardware for different environments.\r\nApply different processing techniques to create DSMs, DEMs, point clouds and orthophotgraphy.\r\nContrast satellite and inertial navigational aids for image registration.\r\nAppreciate the importance of proper flight planning in aerial survey campaigns.\r\nDetail the restrictions and regulations for drone operations in Ireland." . . "Presential"@en . "TRUE" . . "Structured programming"@en . . "10" . "Programming fundamentals: variables, types, expressions and assignment; simple I/O; Conditional and iterative control structures (if statements and while loops); Strings and string processing; Use of class APIs for creating objects and calling methods; Understanding data abstraction and encapsulation; Problem solving: understanding and developing algorithms; Implementing algorithms as simple programs. Introduction to algorithms and data structures. Review of elementary programming concepts suitable for the implementation of abstract data types (operators, types and expressions; control of flow; methods; recursion; input & output); Algorithms for searching: linear, bounded linear and binary searches; Algorithms for sorting: selection, insertion, bubble and quick sorts; Fundamental linear data structures: stacks, queues, linked lists; Object-oriented programming: encapsulation and information hiding, classes, interfaces, class hierarchies, inheritance, polymorphism, basic exception handling; Analysis of basic algorithms.\n\nOutcome:\r\nOn successful completion of the module, students should be able to:\r\nUnderstand, evaluate and create algorithms.\r\nWrite structured programs.\r\nDebug runtime errors.\r\nSelect and implement Data Structures." . . "Presential"@en . "FALSE" . . "Spatial databases"@en . . "10" . "This module delivers an introduction to databases which culminates in a broader treatment of spatial databases. Spatial databases are databases specifically design to manage spatial or geographical data. The module provides an introduction to Structured Query Language (SQL) where no previous experience of SQL or databases is assumed. The module will provide students with a broad understanding of the challenges of working with spatial data and other types of spatially-related information. Using a relational database approach the module considers several key aspects of spatial data science or spatial analysis including:\r\n• relational database models for spatial data;\r\n• spatial data structures; spatial data integrity; spatial data manipulation;\r\n• spatial algorithms used in Geographic Information Systems (GIS);\r\n• spatial analysis techniques including statistical approaches for spatially-informed decision making;\r\n• spatial data visualisation using GIS;\r\n• spatial data formats (PostgreSQL PostGIS database, GeoJSON, ESRI Shapefiles, CSV);\r\n• alternative database models for geospatial data.\r\n\r\nThe course offers students an introduction into spatial and non-spatial SQL with treatments of modern topics in SQL such as Window Functions, query optimization, and so on included. Working with NoSQL and unstructured sources of geospatial data are also discussed.\r\n\r\nThe course offers a mixture of laboratory-based course work and an individual portfolio project which combines SQL and GIS.\n\nOutcome:\r\nOn successful completion of the module, students should be able to:\r\nDesign and implement spatial databases using standard models and spatial database management systems. Query spatial databases using standard query tools and languages\r\nUse Geographical Information Systems (GIS) to analyse and visualise spatial data\r\nCreate interfaces to view and customise, interact with spatial data\r\nDesign and implement spatial indices for efficient searching of data\r\nPerform spatial analysis on large spatial databases and datasets\r\nEnsure reliability, security, integrity and privacy in spatial databases\r\nUnderstand NoSQL model approaches such as key-value stores, document databases, etc\r\nUnderstand the challenges and most effective approaches to working with large databases of spatial data." . . "Presential"@en . "FALSE" . . "Analysing spatial and temporal data using r"@en . . "10" . "This module provides an introduction to the basics of data analysis, exploration and visualisation, with particular focus on spatial and temporal data. The module consists of a series of lectures including an introduction and start-up session to a take away practical exercise using the statistical programming language R. The module begins with basic methods to explore, describe and graphically represent one- and two-dimensional data, before moving on to consider more advanced methods to manipulate and visualise geographical information, and explore and identify trends and seasonal patterns in time series data. In addition, some methodological aspects of data analysis are introduced, in particular the use of open data and ‘citizen science’ data and the idea of reproducibility in data analysis.\n\nOutcome:\r\nOn successful completion of the module, students should be able to:\r\nDemonstrate basic proficiency in coding and data analysis using R, in particular\r\nUnderstand the basics of time series modelling\r\nUnderstand the basics of spatial trend modeling\r\nPerform interactive graphical exploration of spatial and temporal data\r\nCreate interactive web maps, time series visualisations and reports\r\nDemonstrate awareness of the need for critical evaluation of data used in the above." . . "Presential"@en . "FALSE" . . "Geographical information science in practice"@en . . "20" . "This module aims to introduce students to a range of applications that shape the current state-of the art in professional GI Science (GISc) and to show how various elements of GISc can be brought together to solve real-world problems. The module will be taught as a mix of lectures, practicals, workshops and employer visits. This module is intended to extend the theoretical aspects of GISc into practice by taking a problem-oriented approach, incorporating some group-work. The module will include introductions to the design and use of Web-GIS, web apps and Critical GIS. In addition contemporary applications in a range of subject areas will emphasise how geospatial data knowledge shapes society today. A final strand will incorporate a visitor programme from industry professionals to prepare students for work placements and contemporary directions in the GI Science industry. There will also be a significant independent project component to encourage students to engage with contemporary applications in Web-GIS data and technologies to develop their own ideas and knowledge.\n\nOutcome:\r\nOn successful completion of the module, students should be able to:\r\nIdentify a range of contemporary GI Science applications within the GI industry\r\nCritically appreciate the relationship between software, data, and solving problems\r\nManipulate and display spatial information within Web-GIS environments\r\nRecognise the application of GI science and geospatial data in a range of social, business and technical settings." . . "Presential"@en . "TRUE" . . "Satellite remote sensing and earth observation"@en . . "20" . "This module is taught within three broad areas. The first (i) introduces the main concepts of satellite remote sensing including electromagnetic radiation and its interaction at different wavelengths with the atmosphere and surface for both passive and active sensors. A second area (ii) focuses on sensor technology and data acquisition systems of the primary space based remote sensing platforms including; the COPERNICUS missions; Landsat; geostationary satellites; commercial platforms. The final component (iii) focuses on digital image processing - i.e. how images acquired by different satellites are analysed and interpreted to provide information on the Earth. The module is a combination of theoretical and practical based sessions using both commercial and open source software.\n\nLecture Topics include; Applications of Remote Sensing; Historical Development of Remote Sensing; Electromagnetic radiation; Interaction of electromagnetic radiation with atmosphere; Interaction of electromagnetic radiation with a surface; Passive Remote Sensing; Active Remote Sensing; Resolution in Remote Sensing; Pre-processing digital satellite data; Image correction techniques; Spectral Ratioing; Pixel and Object based Classification; Convolution filters; Change detection, Spatial Models, Accuracy assessments.\n\nOutcome:\nOn successful completion of the module, students should be able to:\r\nExplain the factors influencing the generation of electromagnetic radiation.\r\nDemonstrate knowledge of human visual systems, waveband selection and analysis.\r\nDifferentiate remote sensing functionality possible in the visible, near infrared, thermal infrared and microwave portions of the EM spectrum.\r\nIdentify and source the correct satellite datasets for specific applications.\r\nManipulate imagery through rectification, correction and visualisation.\r\nApply and evaluate classification/change detection imagery and quantify accuracy of outputs.\r\nAutomate image processing flow lines for scalable processing of large datasets." . . "Presential"@en . "TRUE" . . "Marine remote sensing - infomar"@en . . "10" . "This module is taught within three broad areas. The first (i) introduces the concept of ocean remote sensing, the marine framework and applications. A second area (ii) will encompass the Irish national seabed mapping programme, INFOMAR (www.infomar.ie), detailing the current and future science and technologies employed in ocean mapping (iii) the third introduces students to different datasets and spatial data management tools for ocean remote sensing. The module is a combination of theoretical and practical based sessions using both commercial and open source software.\nLecture Topics include; INFOMAR overview, ocean science policy framework; historical development of ocean RS; platforms and systems; processing bathymetry and backscatter data, habitat and ecosystem product derivation, satellite derived bathymetry, photogrammetry in the coastal zone, data interpretation, mapping products, data quality framework, data connectivity and impact, stakeholders and users.\n\nOutcome:\r\nOn successful completion of the module, students should be able to:\r\nContrast the science of marine remote sensing with terrestrial techniques.\r\nIdentify key systems and practices used in the field of marine RS.\r\nRecognize the range of integrated data and products associated with Marine Remote Sensing, as well as constraints and limitations, both on individual datasets, and merged products.\r\nDemonstrate an appreciation of mapping scales, data resolutions and density in the context of seabed mapping.\r\nAnalyse system performance characteristics and assess data quality.\r\nSelect and apply suitable seabed mapping workflows. Propose image processing techniques for correcting and analysing marine RS datasets.\r\nDetail the user requirements, stakeholders and added value products in the INFOMAR catalogue.\r\nIdentify the policy framework underpinning ocean science and Identify and source additional marine data and supports via repositories such as the Copernicus Marine Environment Service." . . "Presential"@en . "FALSE" . . "Work placement"@en . . "10" . "Students are required to undertake a minimum of 250 hours work placement between May and the end of July in a company(s) within which they will employ the knowledge and skills learned on the course.\n\nOutcome:\nOn successful completion of the module, students should be able to:\nEmploy the GIS or RS skills that they have acquired during taught modules in a work environment.\nAcknowledge the typical structure and work flow of GIS or RS focused companies.\nManipulate real world data sets for the production of useful products.\nExecute software operation techniques learnt as part of taught modules in a work place environment." . . "Presential"@en . "TRUE" . . "Master of Geograp Information Systems (GIS) & Remote Sensing"@en . . "https://www.maynoothuniversity.ie/study-maynooth/postgraduate-studies/courses/msc-geograpinformation-systems-remote-sensing" . "90"^^ . "Presential"@en . "Given the wider development of Citizen GIS and an increased public awareness and knowledge of the power and value of spatial data, vastly increased amounts of such data from different sources are now available to researchers. However, in order to turn these data into useful information, they must be efficiently managed, processed and analysed before being displayed in a comprehensible format. Geographical Information Systems and the associated field of Remote Sensing greatly aid us in such tasks. The course is equally split between both parts - GIS and Remote Sensing - with four core module introducing the theory and practice of both subject at an introductory and advanced level. Geographical Information Systems or GIS as they are better known, are widely used in a wide variety of subject fields across the physical and social sciences and even in the humanities, with applicability in everything from archaeology and astronomy to geomorphology and globalisation to soil science and social planning. Remote Sensing – the analysis and interpretation of aerial and satellite imagery – has transformed the manner in which we view the Earth. The synoptic view of the Earth that it has given us has greatly improved our understanding of atmospheric and oceanic processes, sustained environmental management and the interaction of humans with the natural world. It is now a standard research tool in many fields such as geology, geography, pollution control, agriculture and climatology. Additional optional modules in Programming, Spatial Databases and Marine Remote Sensing are also available to students who want to develop the technical side more fully, though the course has a strong applied flavour throughout. In addition, all students complete a work placement in the summer months which allows them to gain valuable practical experience to test and develop the skills learnt across the course.\r\n\r\nAims of the Course\r\n\r\n- To provide highly qualified, motivated graduates who have been trained in Geographical Information Systems, Remote Sensing and Digital Image Processing and who can apply the information technology skills they obtain.\r\n\r\n- To produce marketable graduates who will make significant contributions to GIS and RS application areas including; industry, government, academia, the community and voluntary sector and other public and private bodies.\r\n\r\n- To provide an understanding of Geographical Information Systems and Remote Sensing, the technology involved and its applications for specific investigations."@en . . . . "1"@en . "FALSE" . . "Master"@en . "None" . "6500.00" . "Euro"@en . "6500.00" . "Mandatory" . "The MSc in GIS and Remote Sensing is first and foremost a course to skill students for work in a wide range of employment areas. These include a wide range of government and semi-state agencies, local authorities and the voluntary sector, especially in areas associated with the environment and planning. In addition, graduates have worked in a wide range of private sector organisations and businesses, where the ability to work with and critically managed big spatial data is increasingly valued. Successful students have also proceeded to PhD level research and gained employment in academia."@en . "no data" . "TRUE" . "Downstream"@en . . . . . . . . . . . "Faculty of Social Sciences"@en . .