Build production-ready, cloud-native GIS applications using modern Python tools and open-source workflows.
Whether you're a geospatial developer, data scientist, or remote sensing engineer, this hands-on guide will take you from local shapefiles to scalable cloud deployments using the most powerful spatial tools available in 2025.

This is not a theory book. You'll build, automate, and deploy real-world geospatial pipelines step by step — using Python, GeoPandas, Rasterio, Earth Engine, Leafmap, STAC, TiTiler, FastAPI, Streamlit, AWS, and Docker.

What You’ll Master Inside This Book:

Engineering-ready Python GIS stack setup using Conda, pipx, and Docker
Processing shapefiles, GeoJSON, and raster data at scale with GeoPandas & Rasterio
Cloud-based remote sensing pipelines using Google Earth Engine Python API
Serving COGs and dynamic tiles with TiTiler, STAC, and CloudFront
Building interactive dashboards with Streamlit, Deck.gl, and Folium
Automating spatial ETL pipelines with Prefect and Airflow
Deploying FastAPI-based geospatial services on AWS Lambda & GCP Functions
Creating production-ready workflows with CI/CD, GitHub Actions, and Terraform
Real-world projects: Land use detection, asset tracking, urban growth mapping & more

Tools & Technologies Covered

Python 3.10+, GeoPandas, Rasterio, Xarray, Leafmap, Dask
Cloud Platforms: AWS S3, Google Cloud Storage, STAC, TiTiler
Visualization: ipyleaflet, Pydeck, Mapbox GL JS
Automation: Docker, Prefect, GitHub Actions, Terraform
APIs & Apps: FastAPI, Streamlit, Heroku, Serverless Framework

Who This Book Is For

GIS Developers & Engineers
Remote Sensing Analysts
Geospatial Data Scientists
Freelancers & Technical Consultants
Professionals migrating from desktop GIS to modern Python and cloud-first workflows

What Makes This Book Different?

This book goes beyond GeoPandas tutorials — it gives you engineering-grade, modern GIS pipelines that scale from your laptop to cloud infrastructure. Every chapter builds toward real deployment, automation, and production-readiness — no fluff, no theory, just results.

Includes:

Full reproducible Code Representations in Courier New and red formatting
Real-world open datasets with COGs, STAC, GeoParquet, and metadata
Docker, CI/CD, and deployment recipes for Heroku, Lambda, and Streamlit Cloud
Portfolio-ready projects for GitHub and freelance applications

Don’t just analyze maps — build systems that serve them.
If you're ready to future-proof your GIS skillset and deploy real applications, this is the book you’ve been looking for.

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