Overview

Overview#

Software Tools#

In this course, we will use essential software tools that streamline development, version control, and geospatial analysis. Make sure you have these installed and configured:

  • Visual Studio Code: A lightweight, highly customizable code editor with robust extensions for Python development.

  • Git: A version control system for tracking code changes and collaborating on projects.

  • Miniconda or Anaconda: Python distribution platforms that simplify environment management and package installation.

  • QGIS: An open-source GIS software that complements Python tools for geospatial data visualization and analysis.

Tip: If you’re new to any of these tools, refer to their official documentation for setup guides or seek tutorials in the course Software Tools section.

Cloud Computing Platforms#

Geospatial analysis often involves large datasets and computationally intensive tasks. Cloud platforms provide scalable solutions to handle these efficiently. Here are some popular options:

  • Google Colab: A free Jupyter notebook environment with pre-installed Python packages, ideal for coding on the go. We will primarily use this platform in the course.

  • Google Earth Engine: A cloud-based platform for processing geospatial data, especially remote sensing imagery.

  • Amazon SageMaker Studio Lab: Offers free cloud computing resources for Python-based machine learning and data analysis.

  • Microsoft Planetary Computer: Provides geospatial datasets and APIs for large-scale environmental analysis.

Why Use Google Colab?#

  • Accessibility: Works entirely in the browser, so no setup is required.

  • Free Resources: Includes free access to GPUs/TPUs for advanced computations.

  • Integration: Supports popular geospatial libraries like GeoPandas, Shapely, and Leafmap out of the box.

To get started, visit colab.research.google.com and sign in with your Google account.

Python Package Repositories#

There are four primary repositories for installing Python packages:

  • PyPI (Python Package Index): The official repository for Python packages, accessible via pip.

  • Anaconda Cloud: An official platform for hosting and sharing Conda packages.

  • Conda-Forge: A community-driven collection of Conda packages for various domains.

  • GitHub: A platform for hosting code repositories, including Python packages.

Install Python Packages#

There are different ways to install Python packages. The most common methods are using pip or conda. Here are some examples:

To install packages using pip:

pip install leafmap

To install packages using conda:

conda install leafmap -c conda-forge

To install packages from a GitHub repository:

pip install git+https://github.com/opengeos/leafmap.git

To install packages from a requirements.txt file:

pip install -r requirements.txt

uv#

uv is an extremely fast Python package and project manager, written in Rust. For packages that can be installed from PyPI, uv is a great alternative to pip. To install uv, follow the instructions in the official documentation to install uv on your system.

Once installed, you can use uv to create a new environment and install packages as follows:

cd /path/to/your/project
uv venv
uv venv --python 3.12
uv pip install jupyterlab leafmap
uv run jupyter lab

pixi#

pixi is another Python package manager that is written in Rust. It is designed to be fast and efficient, and it can be used as an alternative to conda. To install pixi, follow the instructions in the official documentation to install pixi on your system.

Once installed, you can use pixi to create a new environment and install packages as follows:

cd /path/to/your/project
pixi init
pixi add jupyterlab leafmap
pixi run jupyter lab
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