Spatial Thoughts Newsletter #22
New Intro to QGIS Course, cloud-native geospatial event, open datasets
Thanks for reading my newsletter. Special shoutout to the new subscribers! Read on for new learning resources for QGIS, Python, Google Earth Engine, GDAL and more.
New Introduction to QGIS Course!
We launched a brand new course to help you learn QGIS in a structured way. This course is designed to teach you map making, data visualization, georeferencing, data editing, and spatial analysis by solving practical problems.
As always, the course is completely free for self-study. We have published the full course material openly along with the data package. Check out the course material.
Upcoming Classes
We have 2 new batches scheduled in May. These instructor-led cohort-based sessions offer you the fastest way to ramp up your skills and they come with free lifetime support!
Google Earth Engine for Water Resources Management 10-19 May 2022
Introduction to QGIS (with QGIS.org certification) 14-15 May 2022
Check out the session details and sign-up!
New Learning Materials and Resources
QGIS
We published a new tutorial on Calculating Raster Area in QGIS that uses the ESA WorldCover dataset and shows you how you can quantify landuse and generate reports for any region in the world.
Do you know about QGIS Macros? This is a power-user feature that allows you to customize your QGIS projects. A recent discussion on GIS.SE shows an interesting application on how to use them to prevent accidental deletion of features.
Earth Engine
I wrote a new article explaining how to Manage Earth Engine Assets using the GEE Python API. This article explores the lesser-known
ee.data
module with code examples to help you manage your GEE Assets.A new python library for building web apps called Greppo now supports Google Earth Engine layers. This makes it very easy for Python developers to build UI Apps using GEE - completely in Python. Check out this tutorial on creating a Geospatial app with Google Earth Engine and Greppo.
Python
If you want to learn geostatistics, check out this new book Geographic Data Science with Python written by core developers of the PySAL library. The book is accessible freely on the web.
The field of earth observation is going through a big change with new data formats and capabilities allowing you to process large amounts of data in the cloud leveraging parallel computing. A recent event Cloud-Native Geospatial brought together folks to talk about these new capabilities. You can watch the session recordings online.
Open Datasets
Andy McDonald compiled a list of Public Geological Datasets for Machine Learning in Geoscience that will be useful to anyone working in the oil and natural gas sector.
Neil Southall wrote a twitter-thread with Top 10 Open LIDAR Data Sources. If you are new to LIDAR data, watch this Point Clouds Walkthrough in QGIS video to get started!