Transform Static Maps into Intelligent, Autonomous Systems
The era of static cartography is over. In Geospatial AI (GeoAI) with Python Programming, you will bridge the critical gap between traditional Geographic Information Systems (GIS) and the cutting edge of Artificial Intelligence. This comprehensive volume guides you through the complete lifecycle of modern spatial analysis—from correcting satellite imagery to deploying autonomous agents that can reason about geography.
Whether you are analyzing traffic patterns using Graph Neural Networks or training custom deep learning models to detect infrastructure from space, this book provides the production-ready Python code and theoretical rigor you need to build the next generation of location intelligence.
Core Topics Covered:
- Foundations of Spatial Data: Master the mathematics of Coordinate Reference Systems (CRS), vector manipulation with GeoPandas, and the complexities of map projections.
- Advanced Visualization: Move beyond static plots. Build interactive web maps with Folium and deploy reactive, full-stack geospatial dashboards using Plotly Dash.
- Satellite Image Processing: Unlock the power of Rasterio to handle multi-band satellite imagery. Perform band math to calculate vegetation indices (NDVI) and master masking, clipping, and georeferencing pipelines.
- Deep Learning for Remote Sensing: Train U-Net architectures with PyTorch to perform semantic segmentation of buildings and roads. Implement Meta's revolutionary Segment Anything Model (SAM) for zero-shot feature extraction.
- Spatio-Temporal AI: Model dynamic systems. Use Graph Neural Networks (GNNs) to predict traffic flow on complex road networks and analyze time-series satellite data to track deforestation.
- Autonomous GIS Agents: Build the future. Create LangChain agents capable of "Text-to-Map" reasoning, allowing users to generate geospatial analysis simply by asking questions in plain English.
Capstone Project: The book culminates in a full-scale Automated Disaster Damage Assessment pipeline, integrating image co-registration, AI segmentation, and automated reporting into a cohesive system for emergency response.
All the source code is also on GitHub.
Stop just looking at the map. Teach your code to understand it.
Table of contents
Chapter 1: Coordinate Reference Systems (CRS) - Projections Explained
Chapter 2: The GeoDataFrame - Reading Shapefiles and GeoJSON
Chapter 3: Spatial Operations - Intersections, Joins, and Buffers
Chapter 4: Geometric Manipulations - calculating Areas and Distances
Chapter 5: Plotting Static Maps - Customizing Matplotlib for Geography
Chapter 6: Introduction to Folium - Creating HTML Maps
Chapter 7: Markers and Popups - Adding Data to the Map
Chapter 8: Choropleth Maps - Visualizing Population and Elections
Chapter 9: Heatmaps - Visualizing Density and Traffic
Chapter 10: Dashboards - Combining Maps with Plotly Dash
Chapter 11: Introduction to Rasterio - Reading Satellite Imagery
Chapter 12: Band Math - Calculating NDVI (Vegetation Index) from Space
Chapter 13: Elevation Models - Analyzing Terrain and Slopes
Chapter 14: Masking and Clipping - Extracting Region of Interest
Chapter 14: Masking and Clipping - Extracting Region of Interest
Chapter 15: Time Series from Space - Tracking Deforestation over Time
Chapter 16: Deep Learning on Maps - Detecting Buildings with PyTorch
Chapter 17: Segment Anything (SAM) - Using Meta's AI to Segment Satellite Imagery
Chapter 18: Text-to-Map - Building a LangChain Agent that Generates GeoPandas Code
Chapter 19: Traffic Prediction - Using Graph Neural Networks (GNN) on Road Networks
Chapter 20: Capstone Project - Automated Disaster Damage Assessment with AI
If printed, this ebook would span over 400 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.
Check also the other books in this series
