The Open Geospatial Python Series

Introduction to GIS Programming with Python offers a comprehensive, hands-on introduction to the world of geospatial analysis using Python. Designed for learners of all levels, this book breaks down the complexities of Geographic Information Systems (GIS) into clear, actionable steps, making it ideal for students, researchers, professionals, and self-learners interested in mastering spatial data programming.

Geospatial data has become a key player across numerous fields, including environmental science, urban planning, public health, and business analytics. As the volume and sophistication of this data increase, the need for accessible tools to analyze, process, and visualize it has never been greater. Python, with its rich ecosystem of libraries, is the go-to programming language for working with geospatial data—yet navigating the wide array of libraries and concepts can be overwhelming. This book provides the structure and clarity needed to move from Python novice to confident geospatial programmer.

What sets this book apart is its step-by-step, example-driven approach. Beginning with foundational Python programming skills, you'll build your understanding gradually, progressing to advanced techniques in geospatial analysis. The content is designed to be interactive, with real-world datasets and practical exercises that allow you to apply your skills immediately. You'll work through a variety of projects, from basic spatial data manipulation to building interactive dashboards and cloud-based geospatial applications.

Whether you're looking to automate GIS workflows, develop geospatial web applications, or deepen your spatial data science skills, Introduction to GIS Programming with Python will guide you through the entire process with clarity and confidence.

Who This Book Is For:

• Students and Researchers: In fields like geography, environmental science, urban planning, and data science who need to analyze spatial data as part of their studies or research.
• GIS Professionals: Who want to expand their toolkit beyond traditional desktop GIS software and automate complex workflows.
• Data Scientists and Analysts: Working with location-based data and interested in adding geospatial analysis to their skill set.
• Software Developers: Interested in building applications that work with geospatial data.
• Self-Learners and Career Changers: Motivated beginners looking to break into the growing field of geospatial data science.
• Government and Industry Professionals: Who need to incorporate spatial analysis into their daily work, from urban planning to public health.

What You Will Learn:

• Setting Up Your Development Environment: Tools like Miniconda, VS Code, Git, and Google Colab for geospatial programming.
• Core Python Programming: Including data types, control flow, functions, classes, file handling, and libraries like NumPy and Pandas for data manipulation.
• Geospatial Programming: Hands-on instruction with libraries like GeoPandas, Rasterio, Leafmap, and Geemap for working with vector and raster data, performing geospatial analysis, and creating interactive visualizations.
• Advanced Topics: Cloud computing with Google Earth Engine, hyperspectral data analysis, high-performance geospatial analytics, and distributed computing with Apache Sedona.

Key Features:

• Clear, easy-to-follow explanations and annotated code examples.
• Real-world, authentic datasets to ensure practical learning.
• Hands-on exercises to reinforce each chapter's concepts.
• Guidance on common pitfalls and troubleshooting.
• In-depth coverage of both beginner and advanced topics in geospatial programming.
• Complementary video tutorials and a GitHub repository for additional resources and materials.

By the end of this book, you'll be equipped with the skills to tackle real-world geospatial programming challenges. Whether you aim to build sophisticated spatial data applications, automate geospatial workflows, or simply enhance your analytical capabilities, this book will give you the confidence to succeed in the field of GIS programming with Python.

About the Author:

Dr. Qiusheng Wu is an Associate Professor at the University of Tennessee and an Amazon Scholar. His research focuses on geospatial data science, with an emphasis on using open-source Python tools and cloud computing to study environmental change. He is the creator of several widely-used Python packages, including geemap, leafmap, and geoai, and is dedicated to advancing geospatial analysis and visualization. Dr. Wu's work is a cornerstone of the open-source geospatial community, and he brings his deep expertise to this book, guiding you through the process of learning and mastering geospatial programming with Python.

Master modern spatial data management with DuckDB, the fast and efficient analytical database that's transforming how GIS professionals work with geospatial data. This comprehensive guide takes you from fundamental SQL concepts to advanced geospatial analytics, with hands-on examples using real-world datasets including the US National Wetlands Inventory, Overture global building footprints, and NYC taxi data.

Discover how DuckDB's spatial extension and GDAL integration enable you to process massive geospatial datasets with unprecedented speed and efficiency. Through 14 practical chapters filled with working code examples and step-by-step tutorials, you'll learn to handle Shapefiles, GeoJSON, GeoParquet, and cloud-native formats like PMTiles, while building interactive maps and dashboards that bring your data to life.

Who This Book Is For:

This book is designed for GIS analysts, data scientists, and spatial developers who want to leverage DuckDB for their geospatial workflows. Whether you're transitioning from traditional GIS tools, looking to handle larger datasets more efficiently, or seeking to integrate spatial analysis into modern data pipelines, this book provides the practical guidance you need. A basic understanding of spatial concepts is helpful, but no prior DuckDB or SQL experience is required.

What You Will Learn:

• Install and configure DuckDB with the spatial extension
• Write and optimize SQL queries for spatial data operations
• Integrate DuckDB with Python, Pandas, and Polars
• Import and export geospatial formats: Shapefile, GeoJSON, GeoParquet, PMTiles
• Perform geometry operations, projections, and measurements
• Run spatial joins, point-in-polygon, and nearest-neighbor queries
• Visualize data interactively using Leafmap and Jupyter Notebooks
• Visualize large-scale cloud-native datasets using PMTiles and vector tiles
• Work with case studies on wetlands, buildings, and mobility data
• Build dynamic dashboards with Solara
• Optimize performance for large-scale geospatial processing
• Leverage GDAL for advanced format support and conversions

Key Features:

• Hands-on, example-driven approach with fully working code
• Chapter-end exercises to reinforce learning
• Real-world datasets and case studies throughout
• Seamless Python ecosystem integration (Leafmap, Pandas, GeoPandas)
• Comprehensive coverage of DuckDB 1.4.x spatial features
• Clear, step-by-step workflows for production-ready analytics
• Expert troubleshooting tips and performance tuning insights

By the end of this book, you'll be able to confidently use DuckDB for your spatial data management and analysis workflows, processing datasets of any size, performing complex spatial analysis with ease, and building interactive, high-performance applications that reveal deep geospatial insights.

Learn to apply deep learning and AI to satellite imagery, aerial photos, and geospatial data using Python. This practical, hands-on guide walks you from downloading remote sensing data to training and evaluating deep learning models, all using open-source tools.

What you’ll learn

- Set up a complete GeoAI environment with Python, PyTorch, and GPU acceleration.

- Download satellite imagery from Microsoft Planetary Computer and open data portals.

- Create interactive maps and prepare training datasets from large satellite images.

- Train and evaluate models for seven core geospatial AI tasks: image recognition, object detection, semantic segmentation, instance segmentation, image translation, change detection, and pixel-level regression.

- Apply foundation models, including the Segment Anything Model (SAM), vision-language models, and satellite embeddings, to real-world Earth observation problems.

- Run AI workflows in QGIS without writing code using plugins for tree segmentation, water detection, and more.

Structure and format

- 23 chapters of executable code examples organized in five parts: Foundations, Data Acquisition and Preparation, Core AI Tasks, Foundation Models, and QGIS Plugins.

- All examples use real satellite imagery with PyTorch, torchgeo, segment-geospatial, leafmap, and geoai.

- All code and datasets are freely available on GitHub and Source Cooperative for full reproducibility.

Who it’s for

GIS professionals, remote sensing scientists, data scientists, and students who want to apply AI to geospatial data using Python and open-source tools.