GIS self-study materials

GIS introductions and vocabulary

More learning materials can be found from GeoPortti training materials. CSC other courses can be found from CSC training archive.

Materials of CSC GIS events

Geocomputing in CSC computing environment learning materials

Geocomputing using CSC resources course

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, GDAL and virtual rasters in Taito/Puhti, 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.

Geocomputing webinars

Geocomputing seminars

Geocomputing seminars. 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.

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 supercomputer, 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.

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.

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.