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Geospatial Ai Deep Learning For Satellite Imagery - OneDDL - 09-19-2025 Free Download Geospatial Ai: Deep Learning For Satellite Imagery Published 9/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.18 GB | Duration: 4h 25m Build AI Models for Geospatial Data and Satellite Imagery 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. 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 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! Overview Section 1: Introduction to Geospatial AI and Satellite Imagery Lecture 1 Welcome and Course Overview Lecture 2 Introduction to Geospatial Analysis Lecture 3 Introduction to Artificial Intelligence 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 16 Integrating Google Earth Engine for Data Pipelines 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. 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