GeoAI with Python: A Practical Guide to Open-Source Geospatial AI
GeoAI with Python: A Practical Guide to Open-Source Geospatial AI
Learn to apply deep learning and AI to satellite imagery, aerial photos, and geospatial data using Python.
GeoAI with Python: A Practical Guide to Open-Source Geospatial AI
Artikelnr.: 221732174

GeoAI with Python: A Practical Guide to Open-Source Geospatial AI

Artikelnr.: 221732174

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Learn to apply deep learning and AI to satellite imagery, aerial photos, and geospatial data using Python.
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Wat opvalt

Open-Source Focus
This guide emphasizes open-source tools, allowing users to easily implement geospatial AI solutions without the cost barriers of proprietary software, empowering a broader audience to innovate.
Python-centric Approach
Leveraging Python, the book provides practical examples and extensive libraries, making complex geospatial AI concepts accessible and manageable for both beginners and experienced developers.
Hands-on Applications
With practical projects and real-world case studies, readers gain immediate, applicable skills in geospatial AI, preparing them for real-world challenges and enhancing their employability in an evolving tech landscape.

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Shop GeoAI with Python: A Practical Guide to Open-Source Geospatial AI online at a best price in Belgium. B0GTVFY3PQ
  • 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 learnSet 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 format23 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 forGIS professionals, remote sensing scientists, data scientists, and students who want to apply AI to geospatial data using Python and open-source tools.
Publisher Independently published
Publication date March 24, 2026
Language English
Print length 430 pages
ISBN-13 979-8253507414
Item Weight 2.31 pounds (1.05 kg)
Dimensions 8 x 0.97 x 10 inches (20.3 x 2.5 x 25.4 cm)

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  • vraag: What topics does this guide cover?

    antwoord: It covers deep learning applications for satellite imagery, including tasks like image recognition and change detection.
  • vraag: Is prior programming knowledge required?

    antwoord: Some familiarity with Python is recommended, but the guide provides practical, executable examples.
  • vraag: Where can I access the code and datasets?

    antwoord: All code and datasets are available for free on GitHub and Source Cooperative.

Remote Sensing & GIS Editorial Review

GeoAI with Python: A Practical Guide to Open-Source Geospatial AI is an essential resource for understanding the intersection of artificial intelligence and geospatial analysis. The book is well laid out, offering clear introductions to each topic, practical Python examples, and a unique QR code feature that provides access to all the code used in the book. With 430 pages of content, readers appreciate the step-by-step instructions and exercises that reinforce learning. This comprehensive guide, published independently, is suitable for students and researchers alike, making the powerful world of geospatial AI accessible without expensive software.

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  • Clear and structured layout enhances understanding
  • Practical Python examples for real-world applications
  • Accessible open-source resources included
  • Step-by-step instructions aid learning
  • Exercises at the end of chapters for practice

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  • Might be overwhelming for absolute beginners

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