Course: Geocomputation & Machine Learning for Environmental Applications (intermediate level) – 2025

Our 11th year running!

Dates

On-line teaching: September to November 2025 (8 weeks)

  • Lectures: Starting 16 September until 09 October, every Tuesday & Thursday 11:00 – 14:45 UTC (CEST 13:00, EDT 07:00, PDT 08:00)
  • Catch-up session: Tuesday 14 October at 11:00 – 14:45 UTC (CEST 13:00, EDT 07:00, PDT 08:00)
  • Catch-up session: Thursday 16 October at 11:00 – 14:45 UTC (CEST 13:00, EDT 07:00, PDT 08:00)
  • Lectures resume: 21 October until 13 November, every Tuesday & Thursday 11:00 – 14:45 UTC (21 & 23 Oct CEST 13:00, EDT 07:00, PDT 08:00; thereafter check your time zone due to the end-summer time change)*

* See full day-time list at “preliminary course programme“. All classes will be recorded.

5-day in-person workshop in Matera, Italy

  • 24 – 28 November, coding hackathon (Highly recommended).

For course reviews from last year’s course, please click here for 2023 and here for 2024.

Info

Course programme

On this course, students are introduced to an array of powerful open-source geocomputation tools and machine learning methodologies under the Linux environment. Those with no exposure to programming under Linux are able to gain skills and confidence in using advanced open source data processing routines. Meanwhile, those with a programming background benefit by enhancing their modelling and coding skills. Our aim is to equip attendees with powerful tools and endow them with the ability to continue independent development. The skills acquired on this course will be beneficial, not only for GIS applications but for general data processing and applied statistical computing in various fields. These skills lay the foundation for career development as a geographic data scientist.

Instructors:

Enrollment and Registration Fee Administrator:

  • Juana Mercedes Perlaza Rodriguez, Ph.D. (j.perlaza@spatial-ecology.net)

Course requirements:

The course is aimed at masters or doctoral degree candidates, and researchers and professionals with an interest in spatio-temporal data analysis and modelling. We also accept undergraduate students. Course attendees should be motivated by a desire to learn command line tools to process data.

A basic knowledge of python is expected and can be achieved prior to joining the course through self-taught online tutorials, e.g. https://geo-python-site.readthedocs.io/en/latest/ followed by https://autogis-site.readthedocs.io/en/latest/). Additionally, we expect participants to have a specific interest in geographical data analyses. Therefore, experience in the use of Geographic Information Systems and basic statistical knowledge is essential. Basic concepts of GIS, such as rasters/vectors, overlays, and buffering, as well as fundamentals of statistics, such as mean standard, deviation, and residuals are prerequisites for this course.

Overall, 10-15 hours per week are required outside class time for homework, material review, model implementation, coding, and trouble shooting.

Academic programme:

This course enables students to further develop and enhance their spatio-temporal data processing skills with powerful open source software. Throughout the course students will focus on developing fundamental and independent-learning skills in advanced geospatial data processing – a field that is continuously evolving with the availability of increasingly complex data and ongoing technological advancement. A diverse set of complementary and sometimes overlapping tools will be presented in an overview of open source software available for spatial data processing. We demonstrate their strengths, weaknesses and key features for various data processing objectives (e.g. modelling, data filtering, queries, GIS analyses, graphics or reporting) and data types. Specifically, we guide students in using these tools and software, and assist them along the steep curve of learning command-line programming. We focus our training on helping students to find online help, solutions and strategies, in order to fix bugs, and independently progress with complex data processing problems.

The Academic programme covers different teaching strategies, followed by an in-person workshop:

Online live lectures: (15 min to 1 hour each) Students take part in a series of live lectures introducing the basic functioning of tools, theoretical aspects or background information needed for a better understanding of concepts that are subsequently applied in data processing.

Pre-recorded flipped classroom: (15 min to 1 hour each).  With a flipped classroom, students watch online lectures pre-recorded, and meet the teachers to collaborate in online discussions, addressing doubts, replay to live questions, receiving from the teacher guidance and feedback, thus creating meaningful learning opportunities

Online tutorials: Students are guided during hands-on sessions where instructors perform data analyses on real case study datasets, allowing the former to replicate the procedures on their own laptops.

Online exercises: In addition to tutorials and lectures, students are encouraged to embark on their respective projects of interest during exercise sessions. Specific tasks are set to help reinforce the newly learned data processing skills. These exercises equip students with the confidence and resources to become independent learners, and to effectively address the demands of advanced spatial-data processing. The cross-disciplinary exercises are designed to enhance programming skills and the understanding of mathematical modelling within the context of GIS and Remote Sensing. They may cover forestry, landscape planning, predictive modelling and species distribution, mapping, nature conservation, computational social science and other spatially related fields of studies. Furthermore, these case studies can be considered template procedures and are easily adapted to multi-disciplinary challenges.

Home assignments: We assign homework, which is discussed in the following live online seminar. This allows everybody to benefit from question and answer sessions. Assignments are of two different kinds: suggested assignments and compulsory assignments. The suggested assignments encourage heuristics whereas the compulsory assignments address a specific coding challenge, and are designed for problem solving and critical thinking.

Recorded sessions: all the lectures are recorded and made available for asynchronous viewing, which is particularly helpful to students working across time zones.

In-person workshop: This session is offered in Matera, Italy if at least 10 people are enrolled. We only require proof of travel plans as confirmation of workshop attendance, to be submitted no later than 30 September 2025. The workshop involves individual project development, group collaboration and troubleshooting under direct supervision of the instructors. Attendance at this workshop is not compulsory but strongly encouraged. Matera is a beautiful town, which also offers a wonderful sightseeing opportunity. Flights, accommodation and other travel costs are the student’s responsibility.

Course administration and learning objectives:

With continuous practice throughout the course, students become familiar with command lines and cover numerous topics, including:

  • Learning a large suite of existing tools and knowing which ones to employ for project-specific applications.
  • Acquiring confidence in using several command line utilities for spatial data processing under the Linux operating system.
  • Developing data processing skills; and understanding data types, data modelling and data processing techniques.
  • Independent learning, critical thinking and efficient data processing.

Course requirements: Participants are expected to have intermediate skills in GIS/RS, and python, and a strong desire to learn GIS using open source tools. We assume that participants have an interest in geographical data analyses as well as a basic, working knowledge of calculus and statistics.

Note-taking and organisation:  We urge you to take notes extensively and regularly organise electronic data in order to gain the most from the classes. Curiosity in formulating new research methods, commands and scripting procedures will be fundamental to your overall success.

PC and data Storage: A laptop with at least 40 GB of free disk space and 16GB of RAM.

Class materials: All class material will be presented on www.spatial-ecology.net, under https://spatial-ecology.net/docs/build/html/index.html.

Course certification

At the end of the course, attendees will receive a course certificate, subject to successful completion of the course which requires 100% attendance, home work delivery, and final project presentation (either in-person at Matera or virtually via video link). For university students, course credit approval will be at the discretion of the concerned university.

Syllabus summary

  • OSGeo-live operating system /  Linux bash programming
  • AWK – Gnuplot
  • Gdal/OGR geospatial libraries, PKTOOLS
  • Basic statistical modeling, residuals analysis, error indices
  • Python for Machine Learning regression/classification in supervised framework
  • Students projects presentation.

Preliminary course programme 

The finalised course programme will be released in the coming months. Nonetheless, the preliminary course programme offers a helpful overview of the subject matter.