ITC has a wide selection of courses related to big data technology in its degree, diploma and certificate programmes. Below you can find a selection of the courses with their short descriptions. For more detailed information please refer to the study guide.

Courses

  • Big Geodata Processing

    Thanks to the digital, mobile and sensor revolutions, massive amounts of data are becoming available at unprecedented spatial, temporal, and thematic scales. This leads to the practical problem of transforming big geodatasets into actionable information that supports decision-making at various scales, and that can be further processed to generate new knowledge. In this respect, geodata science workflows and modern data management systems are not only key to processing big geospatial datasets but also to sharing the extracted information and knowledge, and to ensuring the reproducibility of the results.

    In this course, we present methods and techniques that can be used to build solutions that operate with massive and potentially heterogeneous amounts of spatio-temporal data. Building such a bigdata solution requires: 1/ understanding the advantages and limitations of various types of cloud-based solutions, 2/ using a scalable data management system, 3/ building scalable and robust data mining and machine learning workflows that allow processing large amounts of geodata (e.g., to identify data clusters and create data-driven classification and regression models).

Related Courses

  • Scientific Geocomputing

    In this course, the student learns about developing algorithmic solutions to geospatial problems. Turn-key software systems for Geo-information Science and Earth Observation are functionally powerful but have no instant solution to each geospatial problem. The ability to construct custom solutions is an essential capability of the Geoinformatics specialist, who should have competence in addressing geospatial problems by algorithmic solutions. You specifically learn about solution strategies, high-level solution descriptions in pseudo-code, and translations of these into an implementation in some programming language. We will also discuss the scientific side of programming by an introduction into literate programming, which emphasizes documentation of code and the FAIR principles of scientific data management, which apply to data and code. We emphasize the role of data in geospatial algorithms, as these are often data-intensive. By reviewing and developing (pseudo-)code, you will increase your understanding of basic concepts in Geo-information Science and Earth Observation like spatial filters, maximum likelihood classification, coordinate transforms and least squares adjustment. The course’s programming language will be Python, but throughout the Geoinformatics specialization, you will learn to implement your algorithms using also other programming/ scripting languages/environments.

  • Programming Solutions

    Standard geo-data processing can be done using standard functionality offered by standard software tools. But for the solving of complex spatial-temporal problems in earth and environmental research often the handling of (very) large and complex data sets is required. This typically asks for special geoprocessing solutions.

    This course teaches students how to plan and carry out their own programming or scripting project, to support the processing, visualization and analysis of large and complex data sets in their MSc research phase. During the course, students will work on their own geoprocessing challenge, in their own application field and using their own research data.

    Emphasis is on scientific computing using programming (and scripting) languages such as Python, IDL and R. Depending on student interest, also software packages for making scientific 2D and 3D plots in the style of Matlab will be considered. In a similar manner tools for the design of Graphical User-Interfaces (GUI) will be considered, which will allow building interactive windows containing buttons, text boxes, graphs, maps etc.

    Special attention will be given to available statistical and scientific packages for mathematics, science and engineering, such as array processing, linear algebra, regression, optimization, classification, clustering and machine learning.

    The course intends to support individual students in programming solutions that they need during their MSc research. Therefore, certain flexibility is offered to students when to start the course.

  • Advanced Image Analysis

    In this course, you will be introduced to advanced image analysis methods enabling to further enrich your geo-information problem solving abilities. Image processing and analysis methods treated in previous courses, such as conventional hard pixel based classification do not take into account spatial correlations in images and therefore do not exploit information contained in images to full extent. In addition, such methods cannot correctly treat mixed pixels, uncertain class definitions and data from various sources, making them insufficient for reliable geo-information extraction. In this course we aim to treat more specialized image analysis methods. In particular, Support Vector Machines and Random Forest will be introduced for multisource classification at pixel level whereas Convolutional Neural Networks and Markov random fields will be introduced for contextual classification. These methods will be applied to analysis of satellite images at a pixel and some methods at a sub-pixel levels. The methods introduced in this course will be applied on real case studies.

  • Spectral Data Processing

    Earth observation (EO) satellites generate big amounts of geospatial data that are freely available for society and researchers. Technologies such as cloud computing and distributed systems are modern solutions to access and process big Earth observation data. Examples of online platforms for big Earth observation data management and analysis are, just to name a few popular ones, the Google Earth Engine, the Sentinel Hub and the Open Data Cube. This course is on processing remote sensing data from operational and historic missions in an online platform, with specific emphasis on earth science applications. The start consists of basic scripting with Python and, depending on the platform used, JavaScript. Writing own scripts allows to create custom processing solutions, automate such processing chains, apply them to various sources of remote sensing data and provide scalable solutions for handling large data sets. Application to Earth sciences will help you to recognize land forms in images, determine earth surface composition and derive various geophysical parameters from the Earth surface.

Relevant Courses

  • Spatio-temporal Analytics and Modeling
  • Earth Observation for Wetland Monitoring and Management
  • Spatio-temporal Analysis of Remote Sensing Data for Food and Water Security
  • Unmanned Aerial Vehicles for Scene Understanding
  • Extraction, Analysis and Dissemination of Geospatial Information
  • Integrated Geospatial Workflows
  • Statistics for Spatial and Spatio-temporal Data
  • Water, Carbon and Ecosystem Dynamics
  • Cadastral Data Acquisition Technologies and Dissemination Methods
  • Mapping and Monitoring for Natural Resources Management
  • From Data to Geo-information for Natural Resources Management