Geospatial Ai: Deep Learning For Satellite Imagery

Geospatial Ai: Deep Learning For Satellite Imagery
Published 9/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Build AI Models for Geospatial Data and Satellite Imagery

What you’ll learn

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.

Requirements

No prior experience needed! Basic Python knowledge is helpful but not required. 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

Overview

Section 1: Introduction to Geospatial AI and Satellite Imagery

Lecture 1 Welcome and Course Overview

Lecture 2 Introduction to Geospatial Analysis

Lecture 4 Why Python is the Top Choice for AI?

Lecture 5 Overview of Deep Learning in Geospatial Applications

Section 2: Setting Up Your Deep Learning Environment

Lecture 6 Step-by-Step Guide to GPU Setup

Section 3: Cloud-Based AI with Google Colab

Lecture 7 Introduction to Goggle Colab

Lecture 8 Setting Up Google Colab for AI Projects

Lecture 9 Running TensorFlow Models in the Cloud

Lecture 10 Running PyTorch Models in the Cloud

Lecture 11 Saving and Sharing Colab Notebooks

Section 4: Preprocessing Satellite Imagery for Deep Learning

Lecture 12 Calculating Geospatial Indices

Lecture 13 Import and Clean Datasets in Jupyter Notebook with Pandas

Lecture 14 Conducting Zonal Statistics in Python

Lecture 15 Preprocessing Real Sentinel-2 Imagery for Deep Learning

Lecture 17 Working with Large-Scale Geospatial Data

Section 5: Building Convolutional Neural Networks (CNNs) for Geospatial Tasks

Lecture 18 Introduction to CNNs for Satellite Imagery Analysis

Lecture 19 Designing a CNN Model for Crop Health Classification

Lecture 20 Visualizing AI Model Performance

Lecture 21 Evaluating models: Accuracy, precision, recall, and cross-validation.

Lecture 22 Hyperparameter Tuning with Grid Search and Random Search in Python

Section 6: Advanced Geospatial AI Applications

Lecture 23 Building a Convolutional Neural Network for Image Classification

Lecture 24 Building an AI Model for Crop Health Analysis

Lecture 25 Plant Counting with Computer Vision Techniques

Lecture 26 Applying Deep Learning for Global Weather Emulation with FourCastNet

Lecture 27 Validating Biomass Predictions with Ground Truth

Section 7: Course Wrap-Up and Resources

Lecture 28 Course Summary and Key Takeaways

Lecture 29 Next Steps and Additional Resources

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.