Course

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

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

Scope

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). 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 making use of the open science libraries and frameworks - other than the platform’s default.

Contents

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 using raster functions (like band arithmetic, band composition, convolution etc.). Next, we will continue with machine learning (clustering, classification, and prediction) and deep learning (object detection, pixel classification) involving different types of image data (satellite imagery, aerial imagery, oriented imagery). 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 exercise based on the “daily topic” from day one to day 4. In day 2 we will also start an individual course assignment. The student should make a choice for one of following topics and integrate it in his/her own research:

  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 platform, other than Convolutional Neural Network, through ArcGIS Notebooks. For example build your own deep learning model or use OCR (Optical Character Recognition).
  7. Converting non geographic detected bounding boxed to geographic data in RD Coordinate system.
  8. Automation with ArcGIS Notebooks (creating geo-processing tools with ArcGIS Notebooks or planning a task to trigger systematically a Notebook).

    Programme

    Date Activity Focus
    Monday 09 September 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
    Dinner
    Tuesday 10 September 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 11 September 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 12 September Lectures, discussion Supervised learning- Deep learning in ArcGIS platform part 2 (Change and edge detection)
    Exercise a) Edge detection. (Based on a pre-trained model of Esri). b) Change detection on multidimensional image data (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 13 September Presentations Presenting the individual course assignment (present and future work)
    Drinks

    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.

    Exam

    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. See in the appendix the for the StoryMap’s rubric.

    General information

    Registration deadline

    Early bird registration deadline: 09 July 2024
    Regular registration deadline: 09 August 2024

    Registration form

    Target group

    PhDs, Postdocs, Assistant Professors, Associate Professors

    Mandatory required knowledge

    a) Basic ArcGIS Pro skills (Click on this link and follow the free course “Get started with ArcGIS Pro”:). and of course Learn Python in ArcGIS Pro (important fort this course the first two lessons).

    b) It would be a good idea if the participants will get acquainted with machine learning in ArcGIS by following one of more courses from the online learning path of the ArcGIS platform (especially if there is an exercise related to the research topic of the participant): https://learn.arcgis.com/en/paths/try-machine-learning-with-arcgis/

    c) Participants need to bring their own laptops (suggested ram/memory is 32GB) and a good graphical card. See Deep learning frequently asked questions—ArcGIS Pro

    d) 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.

    e) The participants should have at least ArcGIS Pro 3.0 because of the Text Detection and Optical Character recognition capabilities.

    f) The student should install the Deppe Learning frameworks from https://github.com/Esri/deep-learning-frameworks Be aware that the Deep Learning Frameworks installer is dependent of the version of the ArcGIS Pro.

    g) After installing the Deep Learning Frameworks make a clone of your ArcGIS Pro Python environment.

    h) Also very important: download the CUDA toolkit https://developer.nvidia.com/cuda-downloads “a development environment for creating high-performance, GPU-accelerated applications”. The Deep Learning Frameworks which you should install (see above) are GPU-enabled.

    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)

    Fee

    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 proefwageningen.nl/overnachten. 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 8 (eight) weeks prior to the start of the course, cancellation is free of charge.
    • Up to 4 (four) weeks prior to the start of the course, a fee of 50% of the full costs will be charged.
    • In case of cancellation within four 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 EUR will be charged.