This article teaches you how to batch calculate angles for line features in ArcGIS Pro or ArcMap.

How to Batch Calculate Angles of Lines in ArcGIS Pro/ArcMap

The geemap package is built upon ipyleaflet and ipywidgets, and enables users to analyze and visualize Google Earth Engine datasets interactively within a Jupyter-based environment.

geemap: A Python Package for Interactive Mapping With Google Earth Engine

This article teaches you how to set up Visual Studio Code for use with ArcGIS Pro, ArcPy and ArcGIS API for Python.

How to Set Up Visual Studio Code for Use With ArcGIS Pro

This article teaches you how to set up PyCharm for use with ArcGIS Pro, ArcPy and ArcGIS API for Python.

How to Set Up PyCharm for Use With ArcGIS Pro

GeoPolars extends the Polars DataFrame library for use with geospatial data. The main goals of GeoPolars are faster multithreaded operations than GeoPandas and better data interoperability.

GeoPolars: Geospatial Extensions for Polars

Here is a hands-on coding lesson teaching geospatial concepts and Python packages for querying, accessing and processing geospatial data.

Introduction to Geospatial Raster and Vector Data With Python

Here are some tools for working with Google Earth Engine from a Jupyter development environment which provide a foundation for new libraries on top of the Google Earth Engine Python API.

earthengine-jupyter: Google Earth Engine in Jupyter

This article aims to demonstrate how to semantically segment aerial imagery using a U-Net model defined in TensorFlow.

Semantic Segmentation of Aerial Imagery Using U-Net in Python

This book will introduce you to the methods required for spatial programming. It focuses on building your core programming techniques while helping you carry out various geospatial tasks.

PyGIS - Open Source Spatial Programming & Remote Sensing

The geeml Python package makes it easier to extract satellite data from Google Earth Engine using parallel processing and the Google Earth Engine high volume endpoint.

GEEML: Google Earth Engine Machine Learning

This workshop presents and exemplifies a subset of GRASS GIS toolsets for satellite imagery data processing and analysis in combination with other core modules and add-ons.

Free Workshop: GRASS GIS for Remote Sensing Data Processing and Analysis

Check out this four-part series demonstrating how to use machine learning for detecting changes in land cover. Open source libraries and tools are used for this tutorial.

How to Detect Land Cover Changes Using Machine Learning

Take a look here for some free resources on learning QGIS, Python, PyQGIS, Google Earth Engine and GDAL/OGR. You can pick courses that suit your current level of expertise.

Free GIS Learning Paths by Spatial Thoughts

Check here for a Python script for creating an animated GIF, given a shapefile with time-annotated vector objects (e.g., building footprints and construction year).

How to Create an Animated GIF From a Time-Annotated Shapefile

HiRoNEx is a Python tool for automatic, fully unsupervised extraction of historical road networks from historical maps. Check it out here.

HiRoNEx: Historical Road Network Extractor

PyGMT is a Python library for processing geospatial and geophysical data and making publication quality maps and figures. Here is a short course on using PyGMT.

Free Course: Crafting Beautiful Maps With PyGMT

Here is a global downscaled climate data product spanning multiple downscaling methods (source code included), alongside an interactive mapping tool for inspecting, exploring, and comparing the data.

Open Data and Tools for Multiple Methods of Global Climate Downscaling

This website aims to list all open-source (OS) Python libraries that can be used for doing various operations, analyses, visualizations etc. related to GIS and Earth Observation.

Python OS Ecosystem for GIS and Earth Observation

This post describes the process of turning a raster dataset into a SpatioTemporal Asset Catalog (STAC) object using Python.

How to Create STAC Objects Using Python

This talk introduces a geospatial extension to the Parquet file format (GeoParquet) and a geospatial extension to the Arrow columnar format (GeoArrow).

Accelerating Geospatial Computing in R and Python Using Apache Arrow

The scikit-mobility library allows to represent trajectories and mobility flows, manage and manipulate mobility data, extract mobility metrics and patterns, and more. Check it out here.

scikit-Mobility: Human Mobility Analysis in Python

PyGIS is a Python package for installing optional dependencies for geemap and leafmap with only one command.

Introducing a New Python Package Called PyGIS

EOReader is a remote sensing open-source Python library reading optical and SAR constellations, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.

EOReader: Remote Sensing Python Library

Check out these courses on geography, statistics, QGIS, and working with geospatial data in R/Python. These resources could be useful for anyone who integrates geography and statistics.

Free Courses: Geography for Statistics Training

Check out this large collection of tutorials on Google Earth Engine, WhiteboxTools, and more. The blog is created by Dr. Qiusheng Wu, author and contributor of many open source geospatial projects.

Qiusheng Wu's Blog on Geospatial Data Science

Read here about the motivations for using Apache Arrow in geospatial and how it can make geospatial computing faster for everybody.

Building Bridges: Arrow, Parquet, and Geospatial Computing

Check here for a presentation by Dr. Qiusheng Wu on using Python for geospatial analysis and interactive mapping.

Video: Using Python for Geospatial Analysis and Interactive Mapping

pygeometa provides a lightweight and Pythonic approach for users to easily create geospatial metadata in standards-based formats using simple configuration files.

pygeometa: Python Package to Generate Metadata for Geospatial Datasets

This tutorial includes a basic introduction on reading, writing, and creating SpatioTemporal Asset Catalog (STAC) objects using PySTAC.

PySTAC Introduction

PCRaster is a collection of software targeted at the development and deployment of spatio-temporal environmental models. Learn how to use it in QGIS and Python by following these video tutorials.

Video Tutorials: PCRaster Tools in QGIS

This workflow generates automatic contours for agricultural parcels, given Sentinel-2 images. It uses Sentinel Hub to download the imagery and a ResUnet-a architecture.

Field Delineation With Sentinel-2 Data

Check out this tutorial analyzing data extracted from video footage of a football game using the MovingPandas library.

MovingPandas: Football Analysis

Check out this geospatial Python programming course where each lesson is an interactive notebook. It starts from the very basics and introduces the student to GeoPandas, OSMnx, PyQGIS, and more.

Introduction to Python Scripting for Geospatial

Learn how to interact with all the functionality from WhiteboxTools by accessing their Python API. Create stand-alone geospatial applications and workflow automation scripts.

How to Use the WhiteboxTools Python API

Satellighte is a Python image classification library with a purpose to establish a light structure to classify satellite images, but to obtain robust results.

Satellighte: An Image Classification Library With Deep Learning Methods

European Centre for Medium-Range Weather Forecasts (ECMWF) has released more than 40 notebooks to help users reproduce their weather charts. Just click on any chart with a Project Jupyter icon.

ECMWF: List of Jupyter Notebooks for Producing Weather Charts

This session is intended to introduce you to geocoding with practical examples. QGIS native tools and plugins, as well as QGIS Python API classes for geocoding will be considered.

Geocoding for All: A Subtle Introduction to Geocoding

This post shows you how to reshape rasters using the Rasterio Python package and add dimensions to raster arrays.

Changing the Dimensions of Raster Arrays

MovingPandas is a Python library for handling movement data. Get started with learning how to work with the library by launching and running this interactive notebook.

MovingPandas: Getting Started

Introducing a new Python package named geospatial-ml that enables to install commonly used Python packages for geospatial analysis and machine learning with only one command.

geospatial-ml: A Python Package for Installing Common Geospatial Machine Learning Packages

This post shows you how to calculate yearly fire frequency based on MODIS time series. The 8-day MODIS composite products, available from 2002, will be used as input data.

Calculating Fire Frequencies From MODIS Data in GRASS GIS

Here is a tutorial with a interactive notebook for analyzing climate data using Python. The tutorial will help you understand various climate datasets and related terminology.

Google Colab Notebook for Geospatial Climate Data Analysis

Introducing a new Python package named geospatial that enables to install commonly used Python packages for geospatial analysis and data visualization with only one line of code.

geospatial: A Python Package for Installing Commonly Used Geospatial Packages

Dask-GeoPandas is a Python package that brings the support for geospatial data to Dask, merging the geospatial capabilities of GeoPandas and scalability of Dask.

Introducing Dask-GeoPandas for Scalable Spatial Analysis in Python

Easystac is a Python package that provides users of STAC objects as well as clients from Planetary Computer and Radiant ML Hub with an API to query, explore and convert STAC assets to xarray objects.

easystac: A Python Package for Simple STAC Queries

The eemont package extends the Google Earth Engine Python API with pre-processing and processing tools for the most used satellite platforms by adding utility methods for Earth Engine Objects.

eemont: A Python Package That Extends Google Earth Engine

This blog post will teach you an easy way to ingest, manipulate, and visualize Lidar data in Python.

Working and Visualizing Lidar Data in Python

As you upload more and more assets to Google Earth Engine, managing this data becomes quite a cumbersome task. This blog post demonstrates using Python to help with some common data management tasks.

Managing Google Earth Engine Assets Using the GEE Python API

Here is a tutorial teaching you how to generate your own flight path map using Python and open-source data.

Mapping the World's Flight Paths With Python

Have you ever wanted to generate your own river maps using Python and open source data? Well, look no further, here is a full tutorial on just that.

Creating Beautiful River Maps With Python

Spyndex is a Python package that uses the spectral indices from the Awesome Spectral Indices list and allows computing indices with any Python object supporting overloaded operators.

spyndex: Awesome Spectral Indices in Python

The leafmap package is free and open-source, enabling users to analyze and visualize geospatial data with minimal coding in a Jupyter environment, such as Google Colab and Jupyter Notebook.

leafmap: A Python Package for Geospatial Analysis and Interactive Mapping

Learn spatial Python (Python basics, working with data, vector layers, rasters, ArcGIS Pro scripting) with this free book.

Free Course: Introduction to Spatial Data Programming With Python

Follow the instructions here to get started with PyQGIS development with Visual Studio Code on Windows.