. "Geographic Information Science"@en . . "Remote Sensing"@en . . "Environmental sciences"@en . . "English"@en . . "Analytical and numerical methods"@en . . "5" . "The course aims to:\n\nProvide a basic introduction to calculus and basic statistical methods\nProvide an introduction to mathematical and computational methods for modelling applications\nIntroduce general conceptual frameworks for the problems and issues of developing forward and inverse models\nProvide practical analytical and numerical examples for both forward and inverse modelling, particularly linear v non-linear, approaches to solving and generic aspects of implementation\nProvide example applications of the techniques covered, including use of Jupyter Python notebooks\nCover generic issues arising in application of analytical and numerical approaches including the discretisation, detail vs computation time, stochastic processes etc.\nTo provide exposure to numerical tools that are used in a wide range of modelling applications, including an introduction to Bayes Theorem and Monte Carlo methods among others.\nThe module will provide an introduction to a range of fundamental concepts and principles for handling and manipulating data. The first half of the module (taught with CEGE) provides a basic introduction to stats and linear algebra, and important basic concepts; the second half covers slightly more advanced applications of methods and tools for data analysis. The module will cover:\n\nElementary differential and integral calculus and its applications (equations of motion, areas and volumes etc),\nLinear algebra and matrix methods, including computational issues (decomposition for eg) and generalised linear models\nDifferential equations and applications\nOverview of statistical methods including an intro to Bayes Theorem\nNumerical methods, model fitting, numerical optimization Monte Carlo and Metropolis-Hastings\nThe main sessions include:\n\nIntroduction to calculus methods\nIntroduction to linear algebra, matrices\nStatistics and further statistics\nLeast Squares and further least squares\nDifferential equations\nIntroduction to Bayes Theorem\nModel selection\nLinear & non-linear model inversion\nMonte Carlo methods and related numerical tools," . . "Presential"@en . "TRUE" . . "Scientific computing"@en . . "5" . "The module aims to:\n\nImpart an understanding of scientific computing, using Python\nProvide students with a grounding in the basic principles of algorithm development and program construction\nIntroduce principles of computer-based image analysis, information extraction and model development\nThe module will cover:\n\nIntroduction to Jupyter notebooks\nComputing in Python\nComputing for image analysis\nComputing for environmental modelling\nData visualisation for scientific applications\nSee: https://github.com/UCL-EO/geog0111\n\nLearning Outcomes:\n\nAt the end of the module, students should:\n\nHave an understanding of the Python programming language and experience of its use\nHave an understanding of algorithm development and be able to exploit widely-used scientific computing software to manipulate datasets and accomplish analytical tasks\nHave an understanding of the technical issues specific to image-based analysis, model implementation and scientific visualisation" . . "Presential"@en . "TRUE" . . "Geospatial science"@en . . "5" . "Description\nThe course covers generic concepts and techniques that apply to all aspects of geospatial sciences. Starting from a consideration of the nature of mapping and its uses, this also encompasses acquisition methods (including satellite and aerial imagery, laser scanning, positioning systems etc.) and fundamental concepts of reference systems. The course continues by looking at techniques for handling digital map data, and goes on to look at the work of national mapping organisations. The course is delivered through lectures, computer practicals, field trips and workshops.\n\nLearning Outcomes\n\nDevelopment of technical knowledge of the most significant techniques for acquiring spatial data, including the opportunities, challenges and limitations associated with particular techniques;\nDevelopment of appreciation and ability to critically analyse the methods, data and outputs of geospatial endeavours;\nDevelopment of fundamental knowledge of geodesy and reference systems;\nKnowledge and experience in the application of cartographic design principles in the production of mapping for the GIS coursework;\nExperience of problem- and project- definition through individual student-centred coursework brief;\nExperience of providing formative peer feedback through the end of term poster presentation;\nUnderstanding of the issues that arise in developing mapping and charting products from digital data, and the organisational frameworks in which data is acquired and utilised." . . "Presential"@en . "TRUE" . . "Principles and practice of remote sensing"@en . . "5" . "Description\n\nThe module will provide an introduction to the basic concepts and principles of remote sensing. It will include 3 components: i) radiometric principles underlying remote sensing: electromagnetic radiation; basic laws of electromagnetic radiation; absorption, reflection and emission; atmospheric effects; radiation interactions with the surface, radiative transfer; ii) assumptions and trade-offs for particular applications: orbital mechanics and choices; spatial, spectral, temporal, angular and radiometric resolution; data pre-processing; scanners; iii) time- resolved remote sensing including: RADAR principles; the RADAR equation; RADAR resolution; phase information and SAR interferometry; LIDAR remote sensing, the LIDAR equation and applications.\n\nThe course aims to:\n\nProvide knowledge and understanding of the fundamental concepts, principles and applications of remote sensing, particularly the electromagnetic spectrum – what it is, how it is measured, and what it tells us;\nProvide examples of applications of principles to a variety of topics in remote sensing, particularly related to climate and environment\nDevelop a detailed understanding of the fundamental trade-offs in the design and applications of remote sensing tools: spatial, spectral, orbital etc.\nIntroduce new technologies, missions and opportunities, including ground-based sensing, lidar at multiple scales, radar, UAVs, new science and commercial missions, open data and the tools that are emerging to exploit these opportunities;\nIntroduce the principles of the radiative transfer problem that underpins most remote sensing measurements and how it is modelled and solved; applications of radiative transfer modelling to terrestrial vegetation;\nIntroduce students to wider remote sensing organisations, policy and careers through invited seminars from professionals in the field, including former RSEM students.\nSessions .\n\nIntroduction to remote sensing\nRadiation principles, EM spectrum, blackbody\nEM spectrum terms, definitions and concepts\nRadiative transfer principles and assumptions\nSpatial, spectral resolution and sampling\nPre-processing chain, ground segment, radiometric resolution, scanners; poster discussion\nActive remote sensing: LIDAR – principles and applications\nActive remote sensing: RADAR –principles and applications\nNew missions and technologies including LIDAR, UAVs, Copernicus etc.\nApplication discussions around assessed posters" . . "Presential"@en . "TRUE" . . "Research project and dissertation"@en . . "20" . "Many students initially view the successful production of their dissertation as little more than an essential part of the process to obtain their degree. However, your dissertation can be much more than this. It is important that you consider the potential benefits a research-based dissertation offers when planning your project. Your project and the resulting dissertation can provide the following opportunities:\n\nIntellectual independence: A Masters research project gives you a great chance to immerse yourself in a research topic, taking full ownership of it intellectually. You will be able to explore ideas and methods in much greater depth than as an undergraduate student because you will now have the skills and the experience to tackle your research topic much more efficiently, and the concentrated, focused time in which to do so. This is likely to be your first exposure to fundamental research and can be a deeply rewarding and inspiring experience.\nChance to acquire new skills and broaden your horizons: You are likely to obtain greater intellectual satisfaction and improve your employment potential if you use your project as an opportunity to acquire new expertise. Consider how you can develop your practical and analytical skills during the dissertation process, acquire new skills and/or extend existing ones. If you are undertaking a project in a subject area that is relatively new to you, this can extend your knowledge into a new area of expertise. Alternatively, working on something you are already familiar with allows you to increase your depth of understanding in that area. You have a unique opportunity to obtain assistance from supervisors with expertise in several disciplines – use it!\nPossibility to get your dissertation published in a scientific journal. Most MSc dissertations are substantial pieces of work and some are potentially suitable for publication (recent examples can be provided from your course convenor). Just a little extra thought during the preparation of your project and some additional care in writing up could make your dissertation publishable. Your supervisor may be able to offer considerable help in this. If they make a significant contribution to the development of your project, methods of data collection and analysis, or editing, it is reasonable to consider joint publication (you would normally expect to be first author), particularly as they can help with the refereeing process your work will undergo before it is published in the peer-reviewed literature. Publication is of particular benefit to students thinking of going on to do PhDs; published papers are an excellent way to improve your chances of securing PhD funding.\nEstablish key contacts, perhaps with potential employers. If you undertake a project in conjunction with an external organisation, use the opportunity to develop useful contacts. By delivering a competent and professional report in the form of your dissertation, you will impress people who may be in a position to consider you as a future employee. Your university supervisors are also likely to use your project work as a guide to writing a good reference on your behalf" . . "Presential"@en . "TRUE" . . "Environmental gis"@en . . "5" . "is module provides an applied introduction to the use of GIS in the environmental sciences. The course covers the underlying concepts of spatial data and their analyses, and offers extensive hands-on experience of GIS in its application to practical problems and research questions in the environmental sciences.\n\nThe Environmental GIS module commences with an introduction to the concept of GIS and explores the range of software and programming options available. The course provides a foundation in cartography, coordinate systems and data types. The course then progresses through a range of data integration, data management and analytical procedures to provide a hands-on experience of the application of GIS to real environmental problems. The last few sessions focus on some specific case studies, in theory and in practice.\n\nThe main sessions cover:\n\n- Principles of cartography, geovisualisation and geospatial data presentation\n\n- Coordinate systems and projections, and georeferencing\n\n- Types (raster/vector) and sources of spatial data\n\n- Integration and organisation of spatial data in a GIS\n\n- Spatial analyses\n\n- Spatial statistics\n\nThe course is delivered through a series of extended computer-based practicals supported by lecture material, videos, and directed reading. The assessment comprises a 2000 word written report illustrated with independent and original examples of the use, application and analysis of GIS.\n\nThe module assumes no prior knowledge of GIS and geospatial data, but presumes familiarity and competency in general computer use.\n\nThe module delivers a range of core and transferable skills:\n\n- Critical thinking: ability to assess data and ideas\n\n- Problem-solving\n\n- GIS\n\n- Statistical analysis (geospatial statistical analyses)\n\n- Coding (brief experience of coding using Google Earth Engine)" . . "Presential"@en . "FALSE" . . "Mining social and geographic datasets"@en . . "5" . "Description\nWe constantly leave 'digital traces' in our daily lives, both in online and offline worlds; for example our posts in online social networks. Often, this information is associated to specific geographic locations. Examples are GPS trajectories collected using mobile devices or geolocalised posts in online social networks. This data can be collected, analysed and exploited for many practical applications with high commercial and societal impact. This course will provide an overview of the theoretical foundations, algorithms, systems and tools for mining and for discovering knowledge from social and geographic datasets, and, more in general, an introduction to the emerging field of Data Science. The module aims to equip student with the foundations as a data analyst/scientist to be able to analyse a wide array of social and geographic data in the future.\n\nLecture topics will possibly include: introduction to key concepts of data mining; an introduction to computing in Python; spatial network analysis for urban planning/design; mobility analysis and modelling; and an introduction to machine learning techniques on social media and sensing data with real-world case studies and applications." . . "Presential"@en . "FALSE" . . "Surface water modelling"@en . . "5" . "Surface water environments are diverse and include freshwater settings such as lakes and rivers, estuaries and coastal seas and oceans. Surface water bodies typically have a free surface that is exposed to atmospheric influences (including the wind-induced stresses that can be an important driver of circulation), but many surface water problems in hydrology also include interactions with shallow ground waters in both the unsaturated and saturated zones. This course introduces the fundamental principles used to understand the dynamics of water at or near the Earth’s surface and some of the practical challenges in modelling surface water movement, with particular reference to coastal and estuarine waters and river catchments. The course focuses mainly on mechanistic hydrological and hydrodynamic models and includes an overview of some of the mathematical and computational methods used to build simple 1D models, and the application of 2D spatial models to the simulation of tidal surge flooding and climate change impacts / land cover change on river catchment hydrology.\n\nThe module aims to: - outline the principles of surface water modelling - introduce a variety of different mathematical modelling approaches, and the software available with which to implement them, with particular reference to the hydrodynamics of coastal and estuarine systems and catchment hydrology - provide ‘hands on’ experience of advanced modelling software - encourage a critical approach to the evaluation and application of model-based aquatic environmental and climate change science.\n\nThe Surface Water Modelling module commences with an introduction to hydrodynamic modelling (including numerical schemes, dimensionality, boundary conditions and the construction of computational meshes and grids). Practical exercises take students through the coding of a simple 1D tidal channel model, and the implementation of a 2D flood inundation model for an estuarine port. Hydrological modelling is introduced, with particular reference to catchment-scale model applications. The practical element for this part of the module uses the MIKE-SHE modelling system and its application to climate change or land cover change scenario simulation.\n\nThe module also covers key issues associated with the provision of boundary condition data and model validation. The main sessions include: - Hydrodynamic modelling (numerical principles, discretisation, mesh generation, boundary conditions, stability issues) - Coding of a 1D tidal model using Matlab – Use of Blue Kenue and Telemac 2D to create a flood inundation model - Hydrological modelling (catchment-scale models, data requirements, examples and applications) - Catchment modelling using MIKE-SHE - Model validation and application.\n\nThe course necessarily covers some mathematical material (mainly in the introductory lectures) and also makes use of Matlab to demonstrate simple 1D model coding. So some aptitude for and willingness to engage with this kind of material and literature is necessary. However, the assessed practical both use pre-built modelling packages and no computer coding is necessary to complete the assessment." . . "Presential"@en . "FALSE" . . "Terrestrial carbon: modelling and monitoring"@en . . "5" . "The module will cover:\n- The role of vegetation in the climate system\n- Terrestrial vegetation dynamics modelling\n- Remote sensing of vegetation\n- Concepts and maths of data assimilation\n- Using remote sensing data to constrain and test vegetation dynamics models\n\nThe Terrestrial Carbon: modelling and monitoring module aims:\n- To outline the role of vegetation in the carbon cycle and the wider climate system\n- To outline how the vegetation carbon cycle can be modelled and use the models in prediction\n- To provide the linkages between the models and observations\n- To enable the students to use remote sensing (and other) data to constrain, test and criticise the models - To expose the students to modern statistical methods in combining data and models" . . "Presential"@en . "FALSE" . . "Climate modelling"@en . . "5" . "Description\nThe Climate Modelling module aims to introduce and critique a range of models commonly used to understand past and predict future climate change.\n\nThe module will cover the fundamental physics, construction, testing, and use of various climate models. This will range from box models to fully-coupled General Circulation Models (GCMs). The course will explain how physical processes are incorporated within each type of model, as well as examine how models are evaluated and analysed. The module will introduce how future projections are made under a range of scenarios, and then explore the findings of those projections. \n\nThe assessment is focused on analysis and visualisation of future projecitons from the UK’s Earth System Model. These simulations are included in the most recent IPCC report. Students will learn practical skills around visualising large scientific datasets in Python. They will further develop their ability to communicate scientific results through written reports, and to explain technical procedures to users through video tutorials" . . "Presential"@en . "FALSE" . . "Cartography and data visualisation"@en . . "5" . "Description\nMaps and data visualisations play a major role in the way we conceive, interpret and communicate the ever more diverse and complex forms of data collected about the planet. This module will equip students with an in-depth knowledge of the principles of good visualisation and enable them to deploy complex cartographical techniques in the creation of sophisticated maps and graphics. The course will be of interest to any students seeking careers where communicating with data is an important skill.\n\nThe course aims are as follows:\n\n1. To equip and empower students to create robust, reliable and pioneering maps and graphics from a range of innovative sources of data.\n2. To critique current visualisation practice and place it in social and societal context.\n3. Develop students’ ability to apply quantitative skills and use secondary data to understand current social issues and public policy debates.\n4. Develop students’ ability to communicate the complexities/ limitations of data and data analysis.\n\nAfter completing this module students will:\n1. Emulate the latest developments and best practice in data visualisation and mapping.\n2. Develop competencies in different software packages in order to exploit the full breadth of data available for advanced mapping and visualisation.\n3. Become proficient in data storytelling through complex datasets.\n4. Have a portfolio of work that can be used as examples for job interviews or in applications for further study." . . "Presential"@en . "FALSE" . . "Spatial-temporal data analysis and data mining (stdm)"@en . . "5" . "Description\nThis module introduces theories and techniques to visualise, model and analyse (big) spatio-temporal data. Students will be introduced to the topics of statistical modelling, data mining and machine learning, and will learn tools and techniques for spatio-temporal analysis, with an emphasis on application to real world problems. The module content covers a range of topics, which include: Exploratory spatio-temporal visualisation, Statistical modelling and forecasting, Clustering and outlier detection , Machine learning techniques (e.g. Support Vector Machines, Random Forests, Artificial Neural Networks and Deep Learning), Space-time multi-agent simulation, and Social media analysis. Lectures are supported by practical sessions, where real data is used to demonstrate the techniques, with applications such as environment, transport, crime and social media analysis. The software packages used include R (http://www.r-project.org/), SaTScan (http://www.satscan.org/), Python and NetLogo (https://ccl.northwestern.edu/netlogo/). The course is suitable for MSc students in GIS, Geospatial Analysis, Spatio-Temporal Analytics, Smart Cities, Computer Science and related subjects.\n\nLearning Outcomes\n\nUnderstand the basic principles and techniques of spatio-temporal analysis and modelling\nBe comfortable working with spatio-temporal data of different types in different application areas\nBe familiar with using R statistical package for space-time analysis, modelling and visualisation\nHave a working knowledge of other software such as SaTScan and NetLogo.\nBe able to apply the tools and techniques they have learned to new datasets." . . "Presential"@en . "FALSE" . . "Reality capture and precision 3d sensing"@en . . "5" . "Description\nThis course covers advanced topics of 3D sensing. It is comprised of three roughly equal parts: photogrammetry, LiDAR and GNSS. The module introduces the fundamental principles and mathematical concepts for each sensing technique, which are independent of specific applications (airborne, mobile, static…). It then shows how these techniques are used in wide varieties of application from industrial to space-borne. In the first part the course will introduce the mathematical and geometric foundation of photogrammetry, camera calibration and its application. The second part covers theory and practice of producing and validating digital models and from laser scanning (LiDAR). The module also introduces approaches for automated point cloud processing and feature extraction. The third part introduces advanced aspects of the fundamental GNSS principles, applications and integration of GNSS phase observables and other positioning and navigation systems. Special emphasis is placed on the modelling of errors and on the control and assessment of quality.\n\nLearning Outcomes\n\ncompetent to read and follow current research literature on the techniques, technologies and applications of photogrammetry, LiDAR and GNSS\ncritically assess data quality and understand the nature of the errors which affect products\nunderstand capabilities of technologies as well as their limitations\nbe able to derive solutions to given problems of 3D sensing and will have an understanding of the sensor technologies available\nunderstand the concepts, principles and process of point cloud generation and processing" . . "Presential"@en . "FALSE" . . "Sensors and location"@en . . "5" . "Description\n\nBasic principles of operation, applications and integration of sensors used in smartphones and professional geomatic engineering equipment. Location technology with an emphasis on Global Navigation Satellite Systems (GNSSs), but also other radio signals, inertial sensors, digital maps (for map matching), vehicle odometers, compasses, sonar/radar and cameras. Context determination using smartphone sensors. Application of low-cost imaging and 3D imaging sensors to 3D reconstruction and positioning. Students will be introduced to the principles of citizen science and crowd sourcing and how low cost sensors and smart phones can be used to gather data about the urban environment. Strengths (e.g. ability to represent individual views) and issues (data quality, coverage) will be discussed in theory and validated via practical sessions. The aim of this module is to give students a broad understanding of the capabilities of smartphone and geomatics sensors and their application in location, context determination, image understanding and crowdsourcing for both geospatial professionals and consumers.\n\nLearning Outcomes\n\nA broad knowledge of sensors used both by geomatic engineering professionals and by consumers on smartphones, including their basic principles of operation and their applications.\n\nUnderstanding of location technology, including global navigation satellite systems (GNSS) understanding of the strengths and weaknesses of the different location technologies and how to select different combinations of sensors for different location tasks.\n\nUnderstanding of how to use imaging sensors for 3D reconstruction, how to determine context from smartphone sensors and how to crowdsource data." . . "Presential"@en . "FALSE" . . "Master in Remote Sensing and Environmental Mapping"@en . . "https://www.ucl.ac.uk/prospective-students/graduate/taught-degrees/remote-sensing-and-environmental-mapping-msc#course-overview" . "60"^^ . "Presential"@en . "Students develop an all-round knowledge of remote sensing, mapping and data analysis, including fundamental principles, current technological developments and applications to local, regional and global problems. They gain highly developed, marketable practical skills, particularly coding and data analysis, written and other communication skills to enable them to take leading roles in academic, government and industrial sectors"@en . . . . "1"@en . "FALSE" . . "Master"@en . "Thesis" . "14100.00" . "British Pound"@en . "14100.00" . "None" . "Graduates are highly-employable across a wide range of sectors. Recent graduates have been employed in: international space agencies, commercial geospatial and environmental companies; new start-ups using UAVs and satellite data; government agencies; charities and NGOs. The programme is also suitable training for those wishing to undertake a PhD in a quantitative environmental discipline and a number of our graduates have gone on to become leading researchers in the UK and overseas."@en . "1"^^ . "TRUE" . "Downstream"@en . . . . . . . . . . . . . . . . "UCL Department of Geography"@en . .