Machine learning for spatial data

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Machine learning for spatial data

Monday 3 - Friday 7 June 2019


sem.jpgIn this course, participants will learn how to model patterns and structures contained in data. The course will be focused on statistical and machine learning approaches, where the relationships between the observed data and the phenomenon under study are learned directly from observations.

The course will focus on aspects of:

  • data representations
  • feature extraction / reduction
  • clustering
  • semantic classification

Through a series of lectures and practical exercises (in Matlab), the participants will learn about different strategies and their pertinence for specific problems in environmental sciences. Most applications considered in the course will be remote sensing-based, but the course will remain general for a broader audience.

  • Day 1 - morning: Introduction to machine learning, best practices
  • Day 1 - afternoon: Introduction to Matlab scripting
  • Day 2 - morning: lectures on clustering
  • Day 2 - afternoon: practical on clustering
  • Day 3 - morning: lectures on feature extraction and spatial context
  • Day 3 - afternoon: practical on feature extraction and spatial context
  • Day 4 - morning: lectures on supervised classification
  • Day 4 - afternoon: practical on supervised classification
  • Day 5 - morning: lectures on exciting new research topics (structured prediction / deep learning)
  • Day 5 - afternoon: DIY session - come and discuss your data mining problem and design a relevant pipeline
General information
Target Group The course is aimed at PhD candidates, postdocs, and other academics that are interested in machine learning applied to spatial data
Group Size Min. 15 / Max. 25 participants
Course duration 5 days
Language of instruction English
Frequency of recurrence To be determined
Number of credits 1.5 ECTS
Lecturers Prof.dr. Devis Tuia (Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research), Dr Gert Kootstra (Farm Technology, Wageningen University & Research)
Prior knowledge Basic skills in statistics are a plus. The practicals will be in Matlab. A short introduction will be provided on the first day, but previous experience in Matlab or R or Python is required.
Location Wageningen University Campus
Options for accommodation Accommodation is not included in the fee of the course, but there are several possibilities in Wageningen. For information on B&B's and hotels in Wageningen please visit Another option is Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Finally, there are a number of groups on Facebook where students announce subrent possibilities and things like that. Examples include: Wageningen Room Subrent, Wageningen Room Sublets, Room Rent Wageningen, and Wageningen Student Plaza. Note that besides the restaurants in Wageningen, there are also options to have dinner on Wageningen Campus.
Fees 1
PE&RC / WIMEK / WASS / EPS PhD candidates with an approved TSP € 170,- € 220,-
a) All other PhD candidates
b) Postdocs and staff of the above mentioned Graduate Schools
€ 380,- € 430,-
All others € 550,- € 600,-

1 The course fee includes a reader, coffee/tea, and lunches. It does not include accommodation (NB: options for accommodation are given above)
2 The Early-Bird Fee applies to anyone who REGISTERS ON OR BEFORE 6 MAY 2019


  • If you need an invoice to complete your payment, please send an email to, including ALL relevant details that should be mentioned on the invoice (e.g., purchase order no., specific addresses, attendees, etc.).
  • The Early-Bird policy is such that the moment of REGISTRATION (and not payment) is leading for determining the fee that applies to you.
  • Please make sure that your payment is arranged within two weeks after your registration.
  • It is the participant's responsibility to make sure that he/she (or his/her secretary) completes the payment correctly and in time.
PE&RC Cancellation Conditions
  • Up to 4 (four) weeks prior to the start of the course, cancellation is free of charge.
  • Up to 2 (two) weeks prior to the start of the course, a fee of € 170,- will be charged.
  • In case of cancellation within two weeks prior to the start of the course, a fee of € 380,- will be charged.
  • If you do not show at all, a fee of € 550,- will nevertheless be charged.

Note: If you would like to cancel your registration, ALWAYS inform us and do not assume that by NOT paying the participation fee, your registration is automatically cancelled, because it isn't (and do note that you will be kept to the cancellation conditions).

More information

Prof.dr. Devis Tuia

Dr. Lennart Suselbeek (PE&RC)
Phone: +31 (0) 317 485426


To register, please enter your details below and click "Register".