EuroSDR Educational Service (EduServ) annually offers four two-week e-learning courses in the field of GeoInformation (GI). It is designed for knowledge transfer from the research to the production domain. Thus, it is mainly focused on participants from NMCAs and industry, but PhD students and researchers also find the courses interesting as they always reflect the latest developments in GI.

The 23rd series of EuroSDR e-learning courses will begin on March 3-4, 2025 with a pre-course seminar, hosted by the National Land Survey of Finland, Finnish Geospatial Research Institute FGI, Espoo, Finland. During the seminar, background material of four e-learning courses will be presented by the tutors, participants will meet the tutors and fellow participants, and the learning platform – Moodle, will be demonstrated. The four two-week e-learning courses are scheduled from March to June 2025.  They can be followed over the Internet from any location, thereby allowing participants to update their knowledge with minimum disruption. Each course requires about thirty hours of online study.

You can download the flyer here 

From Traditional to AI-based 3D Scene Capture and Modeling  (pdf) 

Tutors: Michael Weinmann (Delft University of Technology, The Netherlands), Dennis Haitz and Martin Weinmann (Karlsruhe Institute of Technology, Germany)

Dates: March 17-28, 2025

Artificial Intelligence (AI) has led to significant breakthroughs in various fields. The advent of implicit, neural-network-based scene representations as well as recent explicit scene representations marks a significant leap in photogrammetric computer vision and novel view synthesis as well as respective applications in robotics, urban mapping, autonomous navigation, virtual/augmented reality, etc. Employing neural networks to efficiently encode high-resolution scene information has been demonstrated to capture precise 3D models, while additionally being more compact than scene representations in terms of point clouds or voxel block models. Through a blend of theoretical insights, visual illustrations and practical exercises, this course will delve into core concepts, implementation strategies, and advanced applications of traditional and AI-based 3D scene capture and visualization, providing you with the skills and knowledge to reflect on the strengths, innovation potential and limitations of current approaches.

Point cloud processing with laser scanning  (pdf)

Tutors: Juha Hyyppä, Joseph Taher, Matti Lehtomäki (Finnish Geospatial Research Institute, Finland)

 Dates: April 7-18, 2025

The development of point cloud generation optoelectronics has been fast in the last decades. The first Airborne Laser Scanners (ALS) were from the early 1990s, followed by Mobile Laser Scanners (MLS) from the early 2000s. Autonomous cars use similar lidar technology for autonomous perception. Previously, Google Tango and, today, iPad Pro include a laser scanner allowing crowdsourced applications. There are also hand-held, backpack and drone systems, including lidars. Terrestrial laser scanning has become a standard tool for providing 3D data in non-built and built environments. This course will provide an understanding of how such point clouds could be processed into informatics. Introduction is given to laser scanning physics and general point cloud processing techniques, and then more focus is given to AI, namely machine-learning and deep-learning approaches in point cloud processing. Several applications are covered, in particular from forestry.

Machine Learning for Earth Observation (pdf)

Tutors: Hao Cheng, John Ray (JR) Bergado, Claudio Persello (University of Twente, Faculty of Geo-Information Science and Earth Observation – ITC, The Netherlands)

Dates: May 5-16, 2025

In recent decades, Machine Learning (ML), particularly Deep Learning (DL), has achieved tremendous success across various domains. This course will begin with a general overview of ML, followed by an exploration of key DL applications in Earth Observation and Geoscience, such as semantic segmentation and change detection using aerial imagery. Step-by-step practical exercises will be provided using Python notebooks. The course is structured into four modules: (i) introduction to ML covering conventional classification methods such as Support Vector Machines (SVM) and Random Forest (RF), illustrated by land cover mapping; (ii) DL with an emphasis on Convolutional Neural Networks (CNNs), illustrated by CNN-based image classification model; (iii) advanced image analysis topics, including semantic segmentation, object detection, instance segmentation, panoptic segmentation, and polygonization; (iv) change detection with the application of neural networks for detecting changes over time.

Spatial Data Quality (pdf)

Tutors: Joep Crompvoets (KU Leuven Belgium), Nienke Eernisse (Ordnance Survey, United Kingdom), Anouk Huisman-van Zijp (Kadaster, The Netherlands), Antonello Rizzo Naudi (Planning Authority, Malta), Angéla Olasz (Lechner Knowledge Center, Hungary), Karin Mertens (NGI Belgium), Anka Lisec (University of Ljubljana, Slovenia)

Dates: June 2-13, 2025

The geospatial landscape has experienced significant transformation, with the volume of geolocated data expanding rapidly. However, data quality can vary widely. Key aspects like accuracy, completeness, and consistency are critical in minimising errors and maximising the value of spatial data across various applications. For national mapping and cadastral agencies, maintaining high standards of spatial data quality is crucial for ensuring the dependability of the information used. To gain better insights in spatial data quality, EuroGeographics Quality KEN and EuroSDR organise a course that is dedicated to this topic. We will explore different data quality elements and methods, look into visualisation challenges, and explore innovative technologies to determine spatial data quality. The course has four modules: (i) Basics of spatial data quality management; (ii) Spatial data quality management: specification, requirements and metadata;  (iii) Quality assurance and evaluation, including measures, methods, usability, trust; (iv) Visualisation of quality in dashboards and crowdsourcing.

Fees

€400 for pre-course seminar + 1 or 2 courses

€500 for pre-course seminar + 3 or 4 courses

€100 for pre-course seminar only

Scholarships

Up to five scholarships will be available to fully cover the course fee. The scholarships are intended for Ph.D./Master students and other applicants with no or very limited financial support from their university or public institution. Successful completion of at least two courses is expected from successful applicants.

In order to proceed with the application:

• Register to EduServ.
• Fill in the application form that includes a motivation letter and information about professional experience. It is recommended to support the application with a reference letter. Both documents shall be submitted to EuroSDR@mu.ie  not later than on January 31, 2025.

Applicants will be informed about the acceptance/rejection of their application at the latest on February 7, 2025. Successful applicants from previous years will be automatically excluded from the evaluation.

Registration

You can register >>> here <<< until February 14, 2025

March 3, 2025 (time zone: Helsinki time, CET +1)

March 4, 2025  (time zone: Helsinki time, CET +1)

* Each session includes a coffee break