Geo-spatial data is the major driver of today’s information society. Smartphones, satellites, and sensors are creating more Big Geo Data than ever. This data is used for an increasing number of scientific purposes that aim to benefit the world. It’s a matter of gathering, analyzing, distributing and visualizing the data to make it fit for specific use, e.g. in systems for improving agricultural practice or creating healthy cities. The technologies supporting these processes are at the core of geoinformatics.
Effectively acquiring and efficiently managing these amounts of data takes more than skills. It also requires keeping pace with ongoing technological developments and understanding how to interpret them. The course Advanced Geoinformatics offers you the possibility to expand your competencies and skills in advanced data acquisition and information extraction methods or focus more on the integration of state-of-the-art methods in geospatial workflows. You will learn to design and develop algorithms, models, and tools to process geo-spatial data into reliable, actionable information.
Earth Observation Science track
Upon completion of this course, you will be able to:
Develop an image processing chain using non-linear filters and mathematical morphology operations for automatic information extraction from images in context of a given problem.
Choose and apply a segmentation method to a given image and describe the uncertainty of the obtained result.
Make informed decisions on the best classification method for a given set of images and a specific problem.
Apply orthorectification to derive orthophoto.
Make informed decisions on appropriate image matching method for a given type of data and problem.
Evaluate attribute and scale uncertainty and relate it to the quality of derived orthophotos, accuracy of resulting image classification and matching.
Explain how point clouds are generated from GNSS, IMU, and range finder measurements and relative sensor registration.
Assess the applicability of laser scanning for various tasks, like surface reconstruction and 3D modelling.
Design survey plans to acquire point clouds taking into account the accuracy and point density requirements.
Evaluate the quality of laser scanning datasets.
Determine and apply optimal point cloud processing methods to extract surface descriptions for geometric modelling and point cloud classification.
Interpret and analyse point cloud processing results.
Geo-information Processing track
Upon completion of this course, you will be able to:
Analyse the quality of structured and semi-structured data sources and apply coding solutions for the storage, querying and curation of this data, appropriate for specific application contexts.
Apply semantic information integration through knowledge formalisation, semantic enrichment, exploratory querying and data mining.
Construct interoperable and reproducible geospatial workflows based on process modelling methods and workflow languages.
Make informed decisions on the infrastructural system design for enabling meaningful data integration on the web.
Discuss the main spatio-temporal modelling paradigms.
Design a conceptual model for a spatiotemporal ABM using UML and the ODD protocol.
Implement a basic ABM model, and calibrate this model using behavioural space.
Explain to peers the main advantages and limitations of using geo-computational methods.
Choose and integrate appropriate geocomputational methods to study a simple spatiotemporal problem.
Organize and conduct the modelling and analysis phases required by a simple spatiotemporal project.
Apply cloud computing approaches to support and/or realize the main modelling and analysis phases.
Evaluate the innovation and societal relevance and impact of the project.
