Learning materials - Services for Research
GIS learning materials
Here we gather materials of GIS courses, webinars and seminars kept at CSC.
General geocomputing materials
Geocomputing using CSC resources
Elias Annila, Eduardo Gonzalez, Johannes Nyman, Kylli Ek / CSC, 2-days course in 2018, 1-day course in 2019.
Introduction to geocomputing in Taito/Puhti and cPouta: using R, Python, Taudem, GDAL and virtual rasters in Taito, setting up a virtual machine to cPouta. In 2018 the course was 1 day longer and included more Taito exercises and whole afternoon about cPouta.
- Slides: 2019, 2018
- Exercise instructions: 2019, 2018
- Exercise data: 2019, 2018
- CSC quick reference for Puhti: 2019
- GIS software and spatial data in Taito (Kylli Ek / CSC, 20 min, 2018)
- Using CSC's cloud services cPouta and Rahti for GIS (Eduardo Gonzalez /CSC, 55 min, 2019)
Allas and Geospatial data (Johannes Nyman / CSC, 60 min, 2020)
Geocomputing seminars in 2018 and 2016. Intro to CSC geocomputing possibilities and short presentations of research projects where CSC geocomputing resources have been used.
Software or application specific courses:
Introduction to Python GIS
Henrikki Tenkanen / HY, 3-days course, 2018.
GIS in Python; Spatial Data Model, Geometric Objects, Shapely, working with (Geo)DataFrames, geocoding and spatial queries, geometric operations, reclassifying data, working with OpenStreetMap data, raster data processing in Python.
- Course web page
- Running Python scripts on CSC's Taito supercluster (Kylli Ek / CSC)
- This course is based on Helsinki University Automating GIS-processes course, which has a few more topics and updates for 2019.
Spatial data analysis with R
3-days course, 2020.
- Vector data with R: spatial operations and spatial analysis, visualizing spatial data (Marko Kallio / Aalto university)
- Raster basics with R: raster data manipulation, map algebra, spatial modelling (Juha Aalto / FMI)
- Spatial analysis with R in Puhti supercluster, running R code in parallel (Kylli Ek / CSC)
Practical machine learning for spatial data
Mats Sjöberg, Markus Koskela, Johannes Nyman, Kylli Ek, 2-days course, 2019.
- Exercises for shallow and deep learning.
Introduction to using Google Earth Engine
Ulpu Leinonen / UTU, 2-days course, 2019.
Google Earth Engine (GEE) is an online platform which allows its users to find, process, analyze, and download satellite imagery and other Earth observation data using Google's infrastructure. The course topics: data types, code editor, accessing satellite imagery, calculations with data, working with vector data, compositing and mosaicking, image classification, time series.
- Course materials
- Demo code for exercise answers
- GEE user guide
- Tutorials in the user guide
- Educational materials
- GEE debugging guide
- GEE Help Forum
- What happens under the hood thread
Introduction to aerial LiDAR data management
Ville Kankare / HY, 1-day course, 2018.
Basic characteristics of LiDAR datasets and how to manage aerial LiDAR datasets using LasTools and R. Predicting forest attributes using area based approach and calculated metrics with R.
Lidar data analysis in Taito, with PDAL and R
Elias Annila, Eduardo Gonzalez, Kylli Ek / CSC, 1-day course, 2019
The main tools covered in the course are: PDAL and different R packages, inc lidR and rlas. The objective is to get a general overview of tasks that can be done using these tools: filtering points, calculating digital elevation and surface models, calculating canopy height, tree detection, mesh creation, change detection.
Web GIS Enabled Spatial Analysis & Data Science with ArcGIS
Aki Kaapro / Esri Finland, 1-day course, 2020
Introduction to the spatial analysis framework within the ArcGIS platform for vector, point cloud, and raster data. Using Esri's ArcGIS Pro desktop app, spatial data science methods are applied for pattern detection and clustering, but also to make spatial data-based predictions and geoAI. How to use modern Web GIS implementation pattern, the new paradigm of how people can share, find, and use geographic information via a geospatial cloud.
- Exercise 1: Get Started with ArcGIS Pro
- Exercise 2: Cluster and outlier analysis
- Exercise 3: Creating a LAS Dataset and Raster Derivatives From Point Cloud Data
- Exercise 3, bonus: New York City 3D lidar
- Exercise 4: Understanding Precipitation Patterns and Trends using Scientific Multidimensional Data, in the text the data link is wrong, use this.