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221732174
GeoAI with Python: A Practical Guide to Open-Source Geospatial AI
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Item #:
221732174
MNT 200528
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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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What Stands Out
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.
Product Details
| 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) |
Product Description
GeoAI with Python: A Practical Guide to Open-Source Geospatial AI
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Customer Questions & Answers
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Question:
What topics does this guide cover?
Answer: It covers deep learning applications for satellite imagery, including tasks like image recognition and change detection. -
Question:
Is prior programming knowledge required?
Answer: Some familiarity with Python is recommended, but the guide provides practical, executable examples. -
Question:
Where can I access the code and datasets?
Answer: All code and datasets are available for free on GitHub and Source Cooperative.
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Features & Benefits
- Hands-on guide to GeoAI using Python and open-source tools.
- Comprehensive setup for GPU-accelerated AI environment with PyTorch.
- Learn to download and process satellite imagery from well-known data portals.
- Train and evaluate AI models for various geospatial tasks.
- Utilize advanced foundation models for practical Earth observation applications.
- All resources and examples are freely available for full reproducibility.
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