Core courses

The core courses provide a strong foundation to all students by teaching the fundamentals of data gathering, processing, analysing and visualisation. The core courses are focussed around six fundamental themes: Location Awareness, Sensing Technologies and Mathematics for Geomatics, Geo datasets & Database Management Systems, Geoweb & Legal Aspects, GIS & Cartography and 3D Modelling, Digital Terrain Modelling (DTM), Photogrammetry & 3D Computer Vision. The core courses are building up from fundamentals and basic skills to application and integration.

Python Programming for Geomatics

Computational thinking

We are living in a world dominated by software. People use their smart phones running software for displaying web maps and finding directions. Massive amounts of data are collected, and processing these data is not a task done manually, but by means of computer programs. Software as become a critical asset in our lives. In the future not knowing the language of computers will be challenging. Similar to being illiterate or innumerate today. Apart from learning to use a programming language, the course Python programming for Geomatics, promotes computational thinking. Computational thinking is how software engineers solve problems. It combines aspects from mathematics, logic and algorithms. Thinking this way teaches you how to tackle problems by breaking them down into smaller, more manageable chunks. This is a skill that every (Geomatics) engineer should be equipped with. Even if you will never code in your professional career, you will benefit if you understand how to think this way.
This course provides a basis for using Python programming as a general purpose programming language (‘a Swiss army knife’) within other Geomatics courses and your professional career. Python is used, because it is a fun and extremely easy to use language, and can be used for a variety of tasks. Also mainstream Geographic Information Systems, such as QGIS or ArcGIS, or database systems, such as PostgreSQL, can be extended by using Python.

For more information, please visit the study guide.

Sensing Technologies and Mathematics for Geomatics

Geodata is key

Laymen are often impressed by glossy 3D city models or flood animations. However, their fitness for use entirely depends on the accuracy and detail of the underlying geo datasets. Indeed high quality geo-data is crucial, as are the piles of concrete for upholding buildings and bridges. As geo-data is key to any geomatics task gaining insight in the characteristics of the sensors that acquire the data is essential. And that is what the course on Sensing Technologies provides. Global Navigation Satellite Systems (GNSSs) head the list of most important geo-data collection techniques already for decades. GNSS is not only used for navigation and sub-centimetre mapping but is also essential for using laser scanners and conducting photogrammetric surveys either by manned or unmanned aircraft. Lidar scanners can be mounted on a tripod – terrestrial laser scanning (TLS) – or on moving platforms such as aircraft, cars and vessels. Measuring range and intensity TLS allows detailed and accurate modelling of a diversity of objects in their full three dimensions. Additionally equipped with GNSS and other navigation sensors, they enable creating accurate 3D models of roads, highways and dikes with high speed. No other sensor technology has become as popular among so many geo-data collectors in such a short time as Unmanned Airborne Systems (UASs). Their rapid rise ensued from a the convergence of micro-electronics, auto-piloting, high-capacity batteries, super materials that are strong yet lightweight, wireless communication, compact digital cameras, image-processing software and miniaturisation of GNSS and other navigation sensors. It is an empirical reality that all measurements contain error and avoiding or removing them is essential. The methods, based on least squares adjustment, were invented by Carl Gauss, famous of his bell-shaped curve in statistics. As a result the modelling and processing of sensor data requires a thorough understanding of mathematics and statistics. 
Geo-data collection, also called land surveying, is one of the oldest professions. Famous  people have started their career as land surveyor, including George Washington, Thomas Jefferson and Abraham Lincoln. As so many others, also these former presidents of the US experienced that geo-data collection is labour intensive and thus costly. Therefore, throughout the ages, automation has been hoisted into the zenith. Photogrammetry, already in existence for more  than 150 years, focussed during its long history on automating the extraction of 3D information of buildings and other objects from planar image coordinates. Today, automation of 3D mapping from images and lasers  heavily relies on fundamental research in the realms of computer vision, artificial intelligence and robotics.

For more information, please visit the study guide.

GIS and Cartography

Learn to look 'under the hood'

Geographical information systems (GISs) were developed in the 1960s to automate the production and the analysis of maps, and were used mostly by goverments to manage natural resources. Nowadays, they are (almost) ubiquitous, and they have become an indispensable tool for engineers, spatial planners, architects, geologists, etc. Moreover, in recent years, Google Earth and other web-based tools have made the use and analyse of maps accessible to everyone. Think for instance of the route planning functions available online or in your telephone.
This course provides an overview of GISs, and of how they can be used in practice to solve real- world problems. You will not learn how to use a specific GIS package (by learning a sequence of buttons to press), we will rather look “under the hood” of a GIS to be able to understand what happens when a button is clicked. You will first learn what a GIS is, its components, and what can be done with one. You’ll be given several real-world datasets commonly used (eg buildings, roads, satellite images, GPS tracks, etc.), and you’ll have to fix errors and integrate them together. We will then explore the concepts under-pinning a GIS. We will cover, among others, the most popular data structures used to store geographical datasets, and we’ll study the algorithms used to extract information from these datasets (eg buffers, spatial interpolation, overlays, etc.). Finally, you’ll also learn how to produce maps---the cartographic principles that permit you to create beautiful and efficient maps will be studied.
The course has both a theoretical part and a practical part, both divided equally in terms of hours spent during the course and in terms of the marking. One particularity of this course is that only free and open-source software is used for the laboratories, in which the concepts seen during the lectures are tested and applied with a GIS. Scripting in Python is also used, so that you can  automate processes in a GIS, or even build programs that would replace totally a GIS. Previous knowledge of a scripting language is required (Matlab or others); if not then the course GEO3001 has to be followed in parallel. 
The course doesn’t target one specific discipline, but rather aims at offering the fundamental skills necessary for different applications. However, each year, there is a group project where students have to solve a specific real-world problem with a GIS. Examples are:

  • how many people in Delft are bothered by the noise from the railway?;
  • what is the optimal location for a new railway between Delft and Pijnacker?;
  • how many people in the Nethelands live within a 15-min bike ride of a train station?

For more information, please visit the study guide.

Positioning and Location Awareness

Where are you and where do you go?

Where are you? Where have you been? Where are you going? This kind of questions are stated nowadays by you, your family, your friends, and others you might even not suspect they are tracking and tracing you. You have to be aware your whereabouts and your activity patterns are connected to.
The theoretical concepts of localisation, location-based applications and services, and the societal and technical push and pull factors are the core elements of this course. Without the framework of global and local reference and coordinate systems you are literally lost. You have to express your own location in context to what is around you from different perspectives and scales. Or, more meaning full: an address (country, city, street, house number) or an indoor location description (building, floor, room-number). This step from a global position to a representation in local context requires profound knowledge of reference systems, coordinate transformations, and geo-coding.
To get a position, or to be positioned, is the second part of this course. Almost any smartphone and tablet is equipped with a kind of a GPS (global positioning system) device. When used within built environments with ‘urban canyons’ the performance is limited. The availability, accuracy, continuity, and integrity of the positioning is not appropriate or guaranteed. For indoor environments – in with we spend 80% of our lifetime – GPS is out of scope. To cover this part of the world there exists a wide range of other localisation techniques, the one better suited than the other, but all with performance limitations. Judging these systems (Wi-Fi signals, RFID, sound, vision, etc.) on their physical characteristics and usability is another learning objective of this course.
People are concerned with the use of location technology in the private and public sector. Recent news on privacy intrusion by security agencies, government, and retailers have increased the awareness of ‘you are being watched’. But at the same time, almost everyone is sharing their location – without realizing – voluntarily to the main smartphone vendors to let app-builders built location-based services to support your daily activities. This innovative trade-off between ‘what is possible’ and ‘what is needed’ is thus to be kept in the borders of a legal and societal context and the final learning objective of this course.

For more information, please visit the study guide.

Digital Terrain Modelling (DTM)

Digital terrain models (DTMs) are computer representations of the elevation of a given area, and they play an important role in understanding and analysing our natural and built environment. They are the necessary input for applications such as: flood modelling, visibility analysis, and effects of climate change on the north poles. In addition, they are relevant for studying the seabed and terrains on other planets such as Mars. 

The course provides an overview of the fundamentals of digital terrain modelling (DTM):

  • different representations of DTMs: triangulated irregular networks (TINs), rasters, point clouds, and contour lines
  • reconstruction of DTMs from different sources (eg LiDAR, photogrammetry, InSAR)
  • spatial interpolation methods
  • conversion between different DTM representations
  • processing of DTMs: outlier detection, filtering, segmentation, and classification
  • applications, eg runoff modelling, watershed computations, visibility analysis
  • techniques to handle and process massive datasets

The course has both a theoretical part and a practical part where students reconstruct, manipulate, process, and extract information from DTMs. 

For more information, please visit the study guide.

Geo Database Management Systems

The era of BIG data

We are entering the era of BIG data. It is widely assumed that further progress and innovation in our society depends on managing and exploiting this data; as expressed by European Commission in the Digital agenda for Europe and in the Dutch context by the recent establishment of the Netherlands eScience Center. Data sources are numerous and increasing, both professional (governmental and commercial) and non-professional (location- aware social media, and smart phone use, sometimes termed VGI or Voluntary Geographic Information). Sensors, surveys, designs (building information models, or BIM) or simulations (for weather predication or water management) generate ever increasing amounts of spatial data. In order to better deal with dynamic phenomena at a wide range of scales the temporal and scale dimensions get more attention. For example in situ sensor networks (monitoring water/ air flow and quality, or traffic in a city) and continuously observing satellites in which the temporal dimension is vital.
The course “geo-database management systems” will first introduce generic tools for database management (SQL) and data modelling (UML) and will then explain how the various types of spatial data can be managed. This includes 2D, 3D and higher dimensional vector data, raster data, and point clouds with emphasize on advanced functionality and BIG datasets. Both open source and commercial systems are covered: PostgreSQL with PostGIS, Oracle with Oracle Spatial, etc. Also, the Dutch database MonetDB will be elaborated on. Data gets meaningful through structure, such as the classification of objects, their properties and their relationships. A road network for example is a highly connected (topological) dataset where each road is connected to junctions. Integration and combination of the various data types is a key aspect; e.g. find all laser scan points from HUGE elevation point cloud that are within 5 meters distance from a given building from the national buildings database.
How can these data be management, including the access rights (who may read, update)?. Support for spatial data types is often missing from traditional database systems where efficient access is often based on sorting; e.g. on social security number or family name. This needs different support for multi-dimensional data.

For more information, please visit the study guide.

3D Modelling of the Built Environment

Create the most efficient 3D models

We see 3D models of houses, terrain, bridges, underground formations, trees, interiors, enclosed spaces, almost everywhere nowadays: in Google Earth, Bing maps 3D, on TV, in the cinema, at exhibitions, on mobile devices. 3D models of real world are created for all kinds of different purposes: either for visualisation to create immersive impressions and allow navigation models, or for design to encourage spatial thinking and creativity, for analysis to investigate various phenomena such as air quality, shadow effects, sky visibility, for simulation of dynamic events such as flood, vegetation growth. The available 3D models vary in realism and resolution, and similarly to the well-known two-dimensional maps can have different accuracy. How can we create 3D models in the most efficient way? Where to start? What kind of sensors can we use to record the details that we need for our application?
Do we use photo images or laser scanning measurements or just use existing 2D maps? How to process the raw data and re-use them existing data? What kind of algorithms we need to apply for houses? Are they the same for the trees? How can we make our 3D model very realistic? What  kind of rules do we have to make our models accurate and correct? Imagine we run a flood simulation. Our 3D houses should be watertight as we don’t want water streaming through the interior. What kind of approach we have to use to store and maintain our 3D models? What shall we do if we have 3D models of our city of in deferent resolution, can we still maintain all together? The data structures should be well-defined and transparent to allow organisations and institutions to re-use 3D data for different purposes. And finally what kind of systems can visualise our 3D models? These and many other challenging questions related to creating, managing, analysing and visualising of 3D models will be discussed in the course 3D modelling of the built environment. The course is organised as a mixture of lectures, workshops and hands on. ln the lectures end labs students can be tested and participate in open discussions. The assignments are organised in such a way that both students loving developing own tools and students mastering conceptual workflows can debate, exchange ideas and learn how things work. The course is given by a very enthusiastic team of lecturers from GIS technology and Design Informatics.

For more information, please visit the study guide.

Geo-information Organisation and Legislation

Provide the key legalisation framework

In the information age, information has become of vital importance to the economic and social development of a country. Especially geographic information is of increasing importance for the successful execution of (public and private) tasks. Professors Onsrud and Rushton convincingly argue that “The value of information comes from its use”. Spatial Data Infrastructures (SDIs) facilitate the collection, maintenance, dissemination, and use of geographic information. By reducing duplication, facilitating integration and developing new and innovative applications, and respecting user needs, SDIs can produce significant human and resource savings and returns and performance gains of both public task and private tasks.
The legal and organisational frameworks are important for the successful use of geographic information. Think about the intellectual property rights such as copyright and the database right, the right to access public data, and the need to respect the privacy frameworks in using data. Also one should bear in mind that the collection, and processing of geographic data requires significant human and financial resources. It is therefore imperative that the geo-processes are organised as efficient as possible: collect it once use it many times. Not only each single organisation is stimulated to adhere to this principle, also at a national, regional (European Union) and global level this would result in significant societal benefits.
However, the needs of communities change over time. While technology may fulfil the new needs, these are often not anticipated by outdated legislation and inflexible organisational structures. Also we have seen an increased role for citizens in the SDI processes. This volunteered geographic information adds a new dimension to traditional structures of cooperation and data exchange.
The Geo-information Organisation and Legislation course provides the key legislative frameworks applying to geographic information and addresses the organisational challenges practitioners may be confronted with when working with geo-information within and between organisations, countries and regions.

For more information, please visit the study guide.

Geo Web, Sensor Networks and 3D-GeoVisualisation Technology

'Internet of things'

Information must flow in order to be used. Information that is not used has no value. The Internet is the ultimate communication channel. We are entering a networked society in which we are all and always connected. This includes the non-humans: Internet of Things. Objects often have a (static or dynamic) location, making the spatial aspect crucial. The re-use of spatial information requires facilities: the geo-infrastructure (GII or geoweb for short). This geoweb can be considered the nerve system of our society. Being connected more and more has one drawback: not understanding all the information (meaning) at the unknown and remote sources. Therefore semantic technologies, such as developed by the W3C, are needed: RDF, OWL and linked data are some of the generic ingredients here. However, humans agree on the information content: the concepts and their labels. Also this requires standardization, but now at semantics level; good examples  are the INSPIRE data specifications of 34 themes.
For real world activities, information from many sources has to be combined. Take for example planning and maintenance of utility networks requiring information on existing other utility networks, the topography, new houses/ connections. Or crisis management urgently needing information about the location of the disaster, the status of the roads, building types, people living their, dangerous goods, utility networks, weather predictions, etc.. There are many aspects to be addressed before information can be shared: How to represent spatial reference systems, coordinates, geometry types, and other attributes? How to know which source has what information? How to request a selection from a webservice? How to encode the response? The standards of the OGC and ISO TC211 are used to build the geoweb in our heterogeneous environment with many different types of devices of makings. The third dimension requires specific attention! The geoweb is not a one-way flow of information as used for data distribution. It can as well be used for data collection; e.g. via VGI (Volunteerd Geographical Information) using the many smartphones or cars with GPS, but also via the many sensors in our world. This later is called the Sensor Web and based on specific sets of standards (SWE). Organizations such as RWS, RIVM and TNO are applying this to realize the Smart-XXX (cities, dikes, roads, etc.) The course “geoweb technology” will introduce the overall architecture, based on protocols such as WMS, WFS, GML, WCS, SLD, SVG, X3D, WebGL. Besides the GIS and DBMS systems covered in other courses, this course will introduce some more: GeoServer, OpenLayers, Layar, Cortona, etc...

For more information, please visit the study guide.

Photogrammetry and 3D Computer Vision

3D computer vision is aimed at recovering and understanding the 3D structure of real-world objects/scenes from visual data (i.e., images, point clouds). This course is about the theories, methodologies, and techniques of 3D computer vision for the built environment, with an emphasis on 3D reconstruction. In the term of this course, the students will learn the basic knowledge and algorithms in 3D computer vision through a series of lectures, reading materials, and lab exercises, each designed as a group assignment with specific objectives and criteria. The topics cover the whole pipeline of reconstructing 3D models from images:

  • Cameras models: how a point from the real world gets projected onto the image plane and how to recover the camera parameters from a set of observations.
  • Image matching: define image features to establish correspondences between images, i.e., finding the same points in image pairs.
  • Structure from motion: recover/refine geometry and camera parameters simultaneously from image correspondences.
  • Multi-view stereo: recover dense geometry (e.g., point clouds) from images.
  • Surface reconstruction: obtain 3D surface models of real-world objects from point clouds.

For more information, please visit the study guide.