Data Science, Artificial Intelligence and Geographic Information Systems (GIS) for Environmental Sciences

Organised by Wageningen Institute for Environment and Climate Research (WIMEK)

Mon 22 May 2023 until Fri 26 May 2023

Venue Wageningen University and Research


This course aims to help the participant explore and understand the ease and benefit of combining the complex (spatial) data science and AI methods and techniques with the powerful capabilities of ArcGIS platform (Esri). This course explores state-of-the-art principles, methods, and techniques related to data science and artificial intelligence applications in relation to the Environmental Sciences’ major topics. We intend to train the participants in open science and towards integrated solutions of data science and Geographic Information Systems (GIS), using different types of spatial and non-spatial data relevant to solving environmental and societal problems. In this way, the participants can give a new dimension to their research by adding a spatial component to their data and being able to process, analyze, combine, and visualize the data in time and space.

Learning goals

The participants will explore potential links between their own research questions and GIS, using their own data or using sample data (remote sensing data or other image and spectra-based information). This training will familiarize the participants with using ArcGIS Pro and develop or integrate a project example or tool within ArcGIS Pro and/or ArcGIS Notebooks(A web-based interactive computing platform working on the Python Environment of the ArcGIS Platform, having the capabilities of integrating open science libraries and frameworks - other than the platform’s default).


The course starts with understanding the data. For example, we will deep dive into image processing, which entails working with multispectral image data, extracting spectral profiles, or raster functions (like band arithmetic, band composition etc.). Next, we will continue with machine learning (clustering, classification, and prediction) and deep learning (object detection, object tracking) involving different types of image data and/or video or camera feeds. To this end, we will use the ArcGIS Pro integrated geoprocessing tools. Furthermore, we will develop or/and integrate scientific algorithms directly on the ArcGIS platform using ArcGIS Notebooks.

The participant will be able to perform all steps jointly and master one selected project according to the following topics:

  1. Data Engineering (Importing, Exploring, Cleaning and Visualization) with ArcGIS Notebooks.
  2. Spatial Analysis with ArcGIS Notebooks- Accessing and creating content in ArcGIS Online.
  3. Machine Learning with ArcGIS Notebooks.
  4. Image processing and Deep learning with ArcGIS Notebooks.
  5. Integrating external deep learning models in ArcGIS with ArcGIS Notebooks.
  6. Using other type of neural networks in ArcGIS (than Convolutional Neural Network) through ArcGIS Notebooks- build your own deep learning model.
  7. Automation with ArcGIS Notebooks (creating geo-processing tools with ArcGIS Notebooks or planning a task to trigger systematically a Notebook).


Date Activity Focus
Monday 22 May Lectures, discussion ‘GeoAI’ in ArcGIS platform (General introduction: the spatial data science workflow- theory and practice)
Lectures, discussion Focusing on using vector, raster and image data in ArcGIS platform
Exercise Exercise a) Data enrichment, data exploration, data visualization; b) understanding image data-spatial, spectral and radiometric resolution, band arithmetic, multispectral versus hyperspectral image data
Tuesday 23 May Lectures, discussion Data Engineering and Machine Learning in ArcGIS Pro: Unsupervised learning (clustering of spatial data) and supervised learning (prediction and classification of spatial data)
Exercise Exercise (data engineering and machine learning exercise in ArcGIS Notebooks)
Individual assignment Start individual course assignment
Wednesday 24 May Lectures, discussion Supervised learning- Deep learning in ArcGIS platform part 1 (Object detection, pixel and feature classification)
Exercise Exercise -Deep learning workflow performed in ArcGIS Pro. (The student can chose to use the geoprocessing tools of ArcGIS Pro or the ArcGIS API for Python in ArcGIS Notebooks to go through the deep learning workflow)
Individual assignment Working on individual course assignment
Thursday 25 May Lectures, discussion Supervised learning- Deep learning in ArcGIS platform part 2 (Change and edge detection)
Exercise Exercise – Change detection. (Based on a pre-trained model of Esri and using the change detector in ArcGIS Pro, the student can calculate the changes between two ‘epochs’ on Sentinel 2 image data)
Individual assignment Working on individual course assignment
Friday 26 May Presentations Presenting the individual course assignment (present and future work)

The students will have one month to finalize their individual course assignment and to write a Story Map. Each student will share his/her Story Map with the course participants through the course Group, in ArcGIS Online or will make his/her Story Map publicly.

The subject of the individual research will be preferably related to the student’s research topic.


The student will write a StoryMap (Esri way of writing articles) based on his/her successfully performing small research during the course. Therefore, the project shall be introduced during the course and delivered after a period of preparation of one month after the course is finished.

General information

Registration deadline

Early bird registration deadline: 22 April 2023
Regular registration deadline: 1 May 2023

Target group

PhDs, Postdocs, Assistant Professors, Associate Professors, professionals from the consortium partners

Mandatory required knowledge

a) Basic ArcGIS Pro skills (Click on this link and follow the free course “Get started with ArcGIS Pro”:

b) Prior to the start of the course, participants will receive course material and instructions.

c) Participants need to bring their own laptops (suggested ram/memory is 32GB).

WUR participants should request an ArcGIS Online account with ArcGIS Pro license at the GeoDesk of WUR if they do not have one yet.

Non- WUR participants may need to install ArcGIS Pro using the licence of their own institutions.

Group size

Minimum: 5
Maximum: 25

Credit points

1.5 EC

Self-study hours

Circa 8 hours (Depending on familiarity with Arc GIS Pro and Python. For those who are familiar enough, self-study is zero)


WIMEK and all other WUR PhD candidates with an approved TSP €70 (early bird) / €120 (regular)
SENSE PhDs with TSP €140 (early bird) / €190 (regular)
All other PhD candidates €180 (early bird) / €230 (regular)
Postdocs and staff of WUR Graduate Schools / graduate schools mentioned above €180 (early bird) / €230 (regular)
All other academic participants €220 (early bird) / €270 (regular)
Professionals from the consortium partners €220 (early bird) / €270 (regular)

The course fee includes coffee, tea and lunch on all 5 days, and dinner on day 1 and drinks on day 5.

The fee does not include accommodation, breakfast and dinner (apart from dinner on day 1). 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. Note that besides the restaurants in Wageningen, there are also options to have dinner at Wageningen Campus.

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 50% of the full costs will be charged.
  • In case of cancellation within two weeks prior to the start of the course, a fee of 100% of the full costs will be charged.
  • If you do not show at all, a fee of 100% of the full costs and a fine of 100 euro will be charged.

Note that if we need to cancel due to Corona, we will re-imburse your fee. We however are not responsible for any accommodation or travel costs you make for attending this course.