Develop your career in GIS and resource management

Sustainable use or conservation of the earth's resources requires the organisation, exploitation and integration of technologies such as database management, image processing and digital cartography, to ensure provision of high quality, reliable and up-to-date information. The Geographical Information Management MSc has been developed in direct collaboration with industry, in response to the increased global demand for multi-disciplinary managers, advisors and consultants in resource management. Taught by a dedicated faculty, this course is unique in providing balanced coverage of the key GIS technologies to prepare you for a successful career across the full range of global sectors using geographical information (GI) technologies.


  • Start dateFull-time: October, part-time: October
  • DurationOne year full-time, two-three years part-time
  • DeliveryTaught modules 40%, group project (dissertation for part-time students) 20%, individual project 40%
  • QualificationMSc, PgDip, PgCert
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

We welcome students from a variety of backgrounds who have a passion for technology and data, an interest in solving real-world problems and making a positive impact. You will acquire experience of world-class spatial problem solving and develop a range of personal and leadership skills to set you on the path for a rewarding career in any one of the growing range of industrial and research sectors that now routinely make use of the GI technologies.

Your career

Successful students develop diverse and rewarding careers in the spatial information industry, national and local government, consultancies, utilities and research organisations. The international nature of this course means that career opportunities are not restricted to the UK. Cranfield graduates develop careers around the world and this course is internationally recognised by employers across the scientific, industrial and educational communities.

Previous students have followed careers in the consulting industry or with government research establishments; whilst others are successfully running their own companies.

Successful graduates have been able to pursue or enhance careers in a variety of key roles such as:

Data Scientist, Academic Researcher, GIS Technician, Geospatial Analyst, GIS officer, Project Development Officer and Remote Sensing Scientist, at organisations such as British Telecom and Cambridgeshire Acre.

Cranfield Careers Service

Cranfield’s Careers Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. We will also work with you to identify suitable opportunities and support you in the job application process for up to three years after graduation.

Cranfield supports international students to work in the UK after graduation

My time at Cranfield prepared me for my current job in a variety of ways. Directly, group projects and working within a team of people who challenge your ideas and viewpoints is infinitely applicable in the work place. Indirectly, the heavy workload prepared me for balancing various projects in the workplace, developing my time management and communication skills.
There are students that are coming up with ideas that I would never have thought of and I know that businesses who we collaborate with would never have thought of. So it’s really important to get idea generation, to get motivations and to get people engaged in what businesses need today.
The assignments have good challenges for each student so we gain more knowledge and information. My group project is tracking precision nutrient management and using different kinds of GIS technology - that case study was in Kenya.

Why this course?

Geographical information management is an exciting and rapidly growing branch of information technology (IT), incorporating and integrating satellite remote sensing, aerial photography and other spatial data, such as soil survey information, to derive reliable and up-to-date information which is essential for the sustainable use or conservation of the earth’s resources. This course will equip you with the skills and knowledge to develop an exciting career helping to address global issues in resource management.

  • Benefit from a thorough training in the technical, analytical and research skills needed for a successful career in this rapidly expanding field.
  • Focus on identifying problems and creating solutions through the selection and integration of appropriate technologies.
  • Develop the essential management skills demanded by employers on our dedicated ‘Management for Technology’ module, delivered by the world-renowned Cranfield School of Management.
  • Learn via a varied combination of lectures, tutorials, real-world case studies and practical sessions led by Cranfield staff and senior visiting lecturers from industry.
  • Undertake individual and group projects, often supported by external companies and focused on your personal interests and career aspirations.
  • Apply the knowledge and skills that you learn on this course to issues such as climate change, improving farming yields, tropical deforestation, transportation, smart navigation systems, disaster response management, recreation, property management and telecommunications.

This MSc is supported by our team of professional thought leaders, including Professor Ronald Corstanje and Professor Jane Rickson who are influential in the field of Environment and an integral part of this MSc.

Informed by industry

The Geographical Information Management MSc designed to meet the current needs of employers and benefits from a strong input from industry experts. This gives our students the confidence to know that what they are learning is both relevant and beneficial to building a rewarding career.

  • The applied GIS and related research our staff undertake is fed back into our GIS teaching programmes, ensuring all students who complete this MSc are equipped with leading edge knowledge and skills.
  • Double accreditation by The Royal Institution of Chartered Surveyors (RICS) and The Chartered Institution of Civil Engineering Surveyors (CICES) ensures that the course has been independently assessed as meeting current professional standards.
  • Employers and partners ensuring that the MSc meets current industry demands for GI specialists include:
  • ERM
  • GIGL
  • WRG
  • WRc PLC
  • Enviros
  • Golder
  • Unilever
  • Neales Waste
  • Natural England
  • National Trust
  • Trucost
  • SLR Consulting
  • FWAG
  • RSPB
  • PA Consulting
  • Joint Research Centre, Ispra
  • Adas
  • Cresswell Associates
  • Chartered Institute of Waste Management
  • Geospatial Insight
  • Oakdene Hollins
  • Chartered Institute of Water and Environment Management
  • Health Protection Agency
  • Highview Power Storage
  • Nomura Code Securities
  • Astrium Geo-information Services
  • Environment Agency
  • Landscape Science Consultancy

Course details

This course comprises eight modules, a group project and an individual project. Courses are not isolated from the real world and many are supported by research groups working on cutting-edge programmes.  


Water course structure diagram

Course delivery

Taught modules 40%, group project (dissertation for part-time students) 20%, individual project 40%

Group project

The group project experience during the course is highly valued by both students and prospective employers. It provides you with the opportunity to take responsibility for a consultancy-type project while working under academic supervision. It also enables you to start building essential industry links and knowledge whilst studying.

The project involves the application and integration of component technologies:

  • GIS
  • GPS and remote sensing
  • Field methods, and statistical analysis to produce quality-assured innovative solutions

Recent group projects include:

Individual project

The individual project is either industrially or University driven. You will be able to select the individual project in consultation with the course team and it will provide the opportunity to demonstrate independent research ability, the ability to think and work in an original way, contribute to knowledge, and overcome genuine problems in relation to the management of the earth's resources. It also offers you the opportunity to work and build links with the types of organisation you will be seeking employment with on successful completion of the course.


Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.

Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course.

Aerial Photography and Digital Photogrammetry

Module Leader
  • Tim Brewer

    Deriving digital elevation models and ortho imagery is an important application of remote sensing data for many areas of spatial work. This module introduces you to techniques for the extraction of topographic information from remotely sensed data using digital photogrammetry techniques. Image interpretation is also a vital skill required in many image based mapping projects. The concepts and techniques of image interpretation will also be introduced and practised.


  • Topographic maps and remote sensing images: map scale and content, image sources and interpretation methods, accuracy issues. 
  • Aerial imagery in the context of other remote sensing systems. Physics of light: principles of recording the image. Stereoscopy and parallax. Geometry: scale variation, relief displacement, tilts.
  • Geometry of vertical aerial imagery: geometry, co-ordinate axes, scale, measurement. 
  • Digital photogrammetry.  Digital elevation models.  Structure from Motion.
  • Satellite photogrammetry.
  • Air photo mosaics and orthophotos.
  • Interpretation: principles and factors.  Applied interpretation: geology, geomorphology, vegetation, soils, urban structures.  Flight planning.  API project management and implementation.
  • Recent developments - UAV imagery, scanning existing photography.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Explain the geometry and spectral properties of vertical aerial photographs.
  • Explain the basic principles of digital photogrammetry.
  • Use aerial photographs in the interpretation of the physical and human environments.
  • Extract elevation data from stereo pairs.
  • Derive orthophotography from standard frame aerial photography.

GIS Fundamentals

Module Leader
  • Tim Brewer

    GIS is an important technology for handling geographic data and has a wide application for studies of the environment. GIS is widely used in many courses, modules, and group and personal projects.  This module therefore provides the opportunity to develop GIS skills that will be of use within your course and in later employment.

    • GIS theory - data structures; data formats; data storage; data standards; spatial and non-spatial data; spatial querying; analysis techniques – reclassification, overlay, proximity, mensuration, visualisation, map algebra; hardware and software; system specification; projections; datums; spheriods.
    • ArcGIS - overview of ArcGIS, ArcMap, ArcCatalog; ArcToolbox, Spatial Analyst.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Describe the functional components of a GIS.
  • Define system specifications including projections, data and process modelling.
  • Organise, using appropriate data structures, geographic data within a GIS.
  • Analyse data and prepare digital databases using GIS software.
  • Summarise, using maps and tables, the results of GIS based analyses. 

Spatial Data Management

Module Leader
  • Dr Stephen Hallett

    Geographical information is now increasingly prevalent in our daily life, affecting personal leisure activities as much as the workplace. Geographical information represents a key theme in environmental management and it has been estimated that some 80% of the data used for environmental, business and policy-oriented decision making is geographical in nature. Such spatial data requires a structured approach in their management if the maximum benefit is to be derived from their analysis and dissemination. This module provides you with a solid introduction to the issues concerning the management of spatial information.

    • Introduction and overview of Spatial Data Management.
    • Database structures – ordered and indexed lists, hierarchical, network, relational, object-oriented, hybrid structure. 
    • Metadata – standards and practice, creation, maintenance, distribution and control.
    • Systems analysis and analysis approaches – methods for designing computerised spatial systems.
    • Object Orientation – theory and practice.
    • Data specification formats, interoperability and handling of geographic data.
    • INSPIRE and the Spatial Data Infrastructure.
    • Industry standard database management systems – Oracle and SQL.
    • Open Source geospatial database management systems – Postgres/PostGIS and SQL.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Design and construct appropriate database structures for GIS analysis using a Geodatabase.
  • Assemble and organise geospatial data within and between a range of database management systems.
  • Understand and critically appraise the application of systems analysis methodologies to spatial data.
  • Examine the role of the INSPIRE Directive for driving interoperability between spatial data infrastructures.
  • Establish, use and evaluate protocols for data and metadata management.
  • Apply and appraise the practical approaches required in managing spatial data. Evaluate and compare approaches using proprietary and open source industry-standard database systems, data and GIS tools.

Environmental Resource Survey

Module Leader
  • Dr Jeroen Meersmans

    This module covers the importance of environmental resource surveys in their requirement to obtain the data used in environmental information management.


    • Introduction to geographical resource survey. Why, when, where and how? Understanding constraints.
    • Introduction to R – a software environment for statistical computing and graphics – and its use in manipulating and visualising survey data.
    • Survey strategies for environmental resources: census with thematic mapping, ground sampling, sampling with property mapping, integrated ground sampling and property mapping.
    • Development of classification schemes – user requirements, data availability, class definitions.
    • Sampling and rapid estimates for plant communities, water and soil quality – biomass, cover and species assessment, count plot methods, plotless sample technique, soil and water survey techniques.
    • Assessment of existing data quality and use in survey design.
    • Statistical design and analysis for environmental resource surveys: area frames, point samples, bulk samples, area samples, sampling at global scales, multi-scale sampling.
    • Quality assessment of environmental data – accuracy measures, effect of bias, quality measures and statistics, error and uncertainty sources and measures.
    • Introduction to interpolation methods, generating maps from point survey data.
    • Integration data sources and types (data fusion) and statistical models with survey data (model data fusion) to increase survey cost effectiveness.  
    • Review of example surveys.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Identify the objective of a survey of the environment.
  • Determine the appropriate survey method to undertake an assessment of environmental resources.
  • Evaluate existing information and models which complement the survey method.
  • Design and conduct field surveys for data collection and verification.
  • Select and carry out appropriate modelling and statistical analyses.
  • Assess the accuracy of results.
  • Summarise and present results of a survey for users effectively.   

Spatial Data and the Internet

Module Leader
  • Dr Stephen Hallett

    Geographical information is now increasingly prevalent in our daily life, affecting personal leisure activities as much as the workplace. Geographical information represents a key theme in environmental management and, indeed, it has been estimated that some 80% of the data used for environmental, business and policy-oriented decision making is geographical in nature. This poses particular challenges to its efficient and timely dissemination to stakeholders and potential end users. Today, the Internet offers a pervasive medium for real-time delivery of geographical information and location-based web services, across a range of computing platforms. Such approaches offer extremely powerful means to reach out to a broad audience of users, particularly those who do not necessarily have the required skills to access or operate specialist GIS software packages. Web mapping offers a means to ‘democratise’ geospatial data – but with a wealth of technical approaches available to develop contemporary web data services and mapping solutions, there needs to be a sound understanding of these choices and approaches if the maximum benefit is to be derived from their analysis and dissemination online. This module provides you with a solid introduction to the issues concerning the management and dissemination of spatial data on the Internet.

    • Introduction to spatial data and the Internet.
    • Principles of web site development.
    • Introduction to HTML. Review of Internet mapping technologies and solutions: Google Maps/Earth API; ESRI JavaScript and Flex API; Open Layers API; Leaflet API; ArcGIS for Server.
    • Introduction to XML/XSLT, GeoJSON.
    • Proprietary and Open-source toolkits.
    • Case study applications of Internet mapping: local authorities, location based services, ‘g-business’.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Appraise contemporary Internet technologies and their application relevant to geographical data dissemination.
  • Apply HTML and CSS to develop web frameworks.
  • Demonstrate the application of JavaScript, and the use of mapping Application Programming Interfaces (APIs).
  • Evaluate of emergent Internet GIS standards and data transfer formats (e.g. GML; XML; GeoJSON).
  • Construct, manage and publish a web site demonstrating spatial technologies.
  • Prepare an Internet mapping web site, and appraise the utility and use of proprietary versus open source contemporary Internet mapping technologies (e.g. ESRI ArcGIS for Server, ESRI JavaScript API, Open Layers API, Leaflet API, MapBox etc.).
  • Evaluate and compare leading proprietary and open source data server technologies (e.g. GeoServer; ESRI ArcSDE; ArcGIS for Server; PostGIS).
  • Examine and debate a selection of real-world internet mapping projects, examples and case studies.

Elective modules
A selection of modules from the following list need to be taken as part of this course

Image Processing and Analysis

Module Leader
  • Dr Toby Waine

    Image processing and analysis are fundamental tools of applied remote sensing.  They are the means by which information is extracted from raw digital data from airborne and space imaging sensors.

    • Principles of optical and radar image formation, image characteristics, statistics and visualisation. Sensors and platforms.
    • Radiometric, spectral and spatial image enhancement. Contrast stretching (linear, bilinear, gaussian, histogram equalisation and manual), digital filtering in the spatial domain (low-pass, high-pass, high-boost, median and directional).   
    • Geometric correction: map projections, selection of ground control points, transform equations, resampling methods (nearest neighbour, bilinear interpolation, cubic convolution). 
    • Supervised and unsupervised image classification: parametric and non-parametric techniques, clustering, segmentation, pixel and object based approaches and validation (accuracy assessment).
    • Post processing, processing chains, change detection and applications.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Identify a wide range of image processing techniques.
  • Explain the purpose of each process and the underlying mathematical principles.
  • Select appropriate image processing sequences to achieve predetermined objectives.
  • Operate and manage an image processing system.
  • Integrate image processing techniques into applications of remote sensing.

Modelling Environmental Processes

Module Leader
  • Dr Andrea Momblanch

    This module will provide you with an introduction to the full suite of environmental models and modelling methods that are currently used to describe and predict environmental processes and outcomes. The module will give you an overview of the different types of models currently being used to describe environmental processes and how they are being applied in practice. The module will also offer you the opportunity to strengthen your analytical abilities with a specific mathematical emphasis, including programming and modelling, which are key skills to launch future careers in science, engineering and technology. In addition, your social skills will be intensively trained throughout the various interactive learning events as well as the group-work based assignment.


    Introduction to the wide range of applications of numerical models in environmental sciences. Lectures will cover examples of models applied in climate, soil, water, natural ecosystems and atmosphere and others.
    Overview of the types of models applied; mechanistic, semi-empirical and empirical models. Why these different forms exist, their strengths and weaknesses. How they are applied?
    Introduction to systems analysis. Overview of the basic concepts and how this relates to model design.
    Introduction to numerical solutions and empirical solutions to model parameterization and calibration.
    Identifying what makes models powerful. Predictions, Scenario and Sensitivity testing.
    Recognizing limits and uncertainties; validating the model. Recognizing the importance of good data.
    Practical applications of environmental models. How this is done, in what programming language?
    Illustrating the impact of models and model outputs on current policy and scientific discourse from global climate change to local flooding risk.

Intended learning outcomes

On successful completion of this module you should be able to:

Examine the major environmental models currently being applied in soil, water, ecosystems and atmosphere.
Identify and evaluate the standard types of numerical models in use in environmental sciences.
Formulate the generic process of model design, building, calibration and validation. Recognize some of the uncertainties introduced in this process.
Evaluate the process of model development might be undertaken in different programming environments.
Undertake a systems analysis. Assess the model building process in the context of the system under consideration.
Construct a model of environmental processes and modify it into a user friendly environment.
Determine the impact and relevancy of environmental models to policy and scientific discourse.


Physical Principles of Remote Sensing

Module Leader
  • Dr Toby Waine

    The appropriate application of remote sensing to the monitoring of earth resources requires an understanding of basic physics and imaging technology.  This subject will introduce you to the basic radiometric concepts and physical relations required for remotely sensed data to be analysed quantitatively.  

    • Introduction to the physical principles remote sensing.
    • Electromagnetic radiation: radiometric units and terms, radiation laws, radiation sources optical, thermal and microwave.
    • Surface interactions.
    • Plant, soil and water spectral properties.
    • Atmospheric interactions and correction.
    • Image formation: passive systems (detectors, opto-mechanical line scanners, waveband separation, linear and area arrays) and active systems (Lidar, RAR and SAR concepts).
    • Spatial resolution and geometry. 
    • Orbits and platforms.
    • Review of satellite and airborne systems.
    • Data reception: data transfer rates, telemetry, ground segment.
    • Data distribution: data suppliers, product levels, internet.
    • Calibration: DN to radiance, irradiance standards, calibration methods.
    • Interpretation of spectral response patterns.
    • Derivation of soil and vegetation indices: ratios, normalised differences, PVI, SBI, tasselled cap concept.  Applications of vegetation indices.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Define the primary physical quantities that are directly related to measured radiance.
  • Define the basic radiation quantities.
  • Discuss the nature of surface and atmospheric interactions with electromagnetic radiation.
  • Define the major types of detectors and describe how satellite images are formed.
  • Analyse the complete remote sensing process from data reception to information extraction.
  • Apply calibration and atmospheric correction methods to image data.
  • Explain the physical relations underlying the retrieval of satellite measured reflectance, temperature and backscattering coefficients.

Applied Environmental Informatics

Module Leader
  • Dr Monica Rivas Casado

    A basis and understanding of methods pertaining to Informatics is needed to effectively obtain information from data. The objective of the module is to supply you with a toolbox of techniques for data mining and modelling (informatics) and develop your strategic ability to effectively apply this toolbox.

    • Introduction to computational methods in informatics; How do we turn data into information?
    • Strategies and approaches to manage large data for computational analysis.
    • Data exploration and data mining. Strategies to elucidate underlying structures in the data. Are these causal or coincidental? How does one interpret and communicate results from a data mining exercise effectively.
    • Inference modelling. Generating quantitative models which can be deployed on existing or new data to generate the required information. Identifying the appropriate model type, form and configuration. Developing the technical skill to configure and deploy inference engines.
    • Process, empirical or semi-empirical modelling. To use existing process based or (semi) empirical models to generate information from data. Identifying and understanding the constraints of each type of model. Developing the technical skill to configure and deploy these models. Introduction to model data fusion methods and their applications.
    • Understanding how models operate in space and over time and how spatial and/or temporal effects can affect model behaviour.
    • Error and Diagnostics. How to assess model performance. Validation procedures and measures. Sources of uncertainty (data/model/deployment). Using model performance measures as diagnostic tools for optimal model configuration and on-going quality control.
    • Effective communication of the computational Informatics processes and outcomes. Statement of quality and remit of the modelling development process. Identifying and communicating what a particular model can and cannot do.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Assess the potential and potential pitfalls of ‘big data’.
  • Assemble and organise data for prescribed analysis and modelling approaches.
  • Appraise and apply data mining techniques, identify underlying data structures.
  • Construct models that reproduce observed relationships; the application of inference engines.
  • Create integrative designs of process models with data; applying model data fusion.
  • Recognise uncertainty and error in data and model parameter estimations.
  • Develop diagnostics measures of model performance.

Advanced GIS Methods

Module Leader
  • Tim Brewer

    GIS analyses are based upon increasingly sophisticated methods, but the results are subject to both error and uncertainty. You will be introduced to a range of advanced methods that you can use in your group and thesis projects, and then in your future career. Emphasis will be given to the role of GIS in modelling environmental systems and the programming tools available to develop applications.

    • Spatial analysis: multi criteria analysis, hydrological modelling, digital terrain modelling, network analysis, linear referencing.
    • Python scripting concepts.
    • The Python editor in ArcGIS and as a standalone program.
    • Python syntax.
    • Scripting procedures.
    • Variables, constants and data types.
    • Writing simple scripts.
    • Error handling.
    • Processing files.
    • The object model in GIS.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Assess the quality of geographic data.
  • Undertake advanced spatial analyses.
  • Analyse the requirements of a proposed application and synthesise an appropriate solution.
  • Develop scripts to efficiently run complex/time consuming processes.

Landscape Ecology

Module Leader
  • Professor Ronald Corstanje

    “Landscape ecology emphasizes the interactions between spatial patterns and ecological processes, that is, the causes and consequences of spatial heterogeneity in a range of scales” (Turner et al. 2001). Landscape ecology provides a foundational framework for problem solving, decision making and planning in land restoration, ecological conservation and natural resources management. It covers topics related to structure, function and change, and it provides the necessary tools to select the appropriate methods to test spatial hypothesis and solve problems at multiple scales. This module is designed to introduce you to a variety of tools that measure and quantify landscape components at different scales, and to understand them in the context of their field of expertise priorities and regulations.


    • Introduction to landscape ecology
    • Landscape elements (e.g. mosaics, corridor and patches)
    • Landscape metrics (e.g. spatial pattern metrics)
    • Landscape fragmentation, connectivity, scale and hierarchy
    • Species population and sampling, and National vegetation classification
    • Introduction to point pattern analysis: Ripley’s K Function
    • Spatial aggregation

Intended learning outcomes

On successful completion of this module you should be able to:

  • Explain the key elements of a landscape.
  • Discuss the importance of scale in landscape ecology related questions.
  • Design strategies to quantify spatial patterns, spatial structures, and species at the relevant scales.
  • Select the appropriate quantitative methods to test spatial hypotheses, solve problems, inform monitoring programs, and interpret the findings in the context of conservation priorities and conservation law.
  • Evaluate monitoring data to guide decision making in ecosystem management.


The MSc in Geographical Information Management is accredited by the Royal Institution of Chartered Surveyors and the Chartered Institution of Civil Engineering Surveyors.

Accreditation provides you with the assurance that the course has been independently assessed as meeting professional standards.

RICS accredited course logo

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