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Geospatial AI: Deep Learning for Satellite Imagery
Rating: 3.5 out of 5(37 ratings)
6,480 students

Geospatial AI: Deep Learning for Satellite Imagery

Build AI Models for Geospatial Data and Satellite Imagery
Created bySenior Assist Prof Azad Rasul
Last updated 6/2026
English

What you'll learn

  • Preprocess satellite imagery for AI using Python and Google Earth Engine.
  • Build and train CNNs for geospatial tasks like crop health classification.
  • Apply deep learning to analyze satellite data for real-world applications.
  • Evaluate and optimize AI models with metrics and hyperparameter tuning.

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Course content

7 sections • 30 lectures • 4h 40m total length

  • Welcome and Course Overview5:10

    What you'll build, who this is for, how to follow along

  • Introduction to Geospatial Analysis:7:10
  • Deep Learning in Geospatial Applications13:48
  • Introduction to Artificial Intelligence4:19
  • Why Python for Geospatial AI?2:47

    Discover why Python dominates AI and ML with a rich ecosystem—NumPy, SciPy, pandas, scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn—offering readable syntax, rapid prototyping, and cross-platform reliability.

  • Geospatial AI fundamentals

Requirements

  • Basic Python knowledge is helpful. You'll need a computer, internet access, and a free Google account for Google Colab. All tools and datasets are provided in the course!

Description

Transform satellite imagery into actionable insights with Geospatial AI!
Dive into Geospatial AI: Deep Learning for Satellite Imagery and master the art of building AI models for geospatial analysis. This hands-on course equips you with cutting-edge skills to process Sentinel-2 imagery, design convolutional neural networks (CNNs), and tackle real-world challenges like crop health analysis, plant counting, land cover classification, and global weather emulation using FourCastNet.

Begin with Python and AI fundamentals, then advance to powerful tools like Google Colab, Google Earth Engine, TensorFlow, and PyTorch for handling large-scale geospatial data. Learn to preprocess satellite imagery, calculate geospatial indices, conduct zonal statistics, and optimize models through hyperparameter tuning and cross-validation. Compare deep learning with traditional machine learning methods like Random Forest to understand their strengths in geospatial contexts.

The course culminates in a capstone project where you’ll build a portfolio-ready land cover classification model, integrating data acquisition, preprocessing, and AI modeling. Perfect for data scientists, GIS professionals, or ML enthusiasts with basic Python and machine learning knowledge, this course bridges theory and practice to elevate your career in geospatial AI.

Practical learning awaits! Through guided projects and quizzes, you’ll apply AI to solve pressing geospatial challenges, from monitoring deforestation to optimizing agricultural yields, preparing you to make a tangible impact in this dynamic field.

Enroll today to unlock the future of satellite imagery analysis and become a geospatial AI expert!

Who this course is for:

  • Beginner Data Scientists: New to AI and geospatial analysis, eager to learn deep learning for satellite imagery.
  • GIS Professionals: Looking to integrate AI into geospatial workflows for tasks like land cover or crop analysis.
  • Environmental Researchers: Interested in applying CNNs to satellite data for climate or agricultural studies.
  • Students and Hobbyists: Curious about geospatial AI, with basic Python skills or a willingness to learn.

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