[100% Off] Geospatial Ai: Deep Learning For Satellite Imagery
Analyze Sentinel imagery with Python, Google Earth Engine, CNNs, and U-Net for classification and segmentation.
What you’ll learn
- Preprocess Sentinel-2 imagery for deep learning using Python and Google Earth Engine.
- Calculate geospatial indices and summarize raster data using zonal statistics.
- Build CNN models for satellite image classification and crop health analysis.
- Evaluate models using accuracy, precision, recall, and cross-validation, and tune their hyperparameters.
- Build and train a U-Net model for semantic segmentation.
- Visualize segmentation predictions and export results as GeoTIFF files.
- Apply Sentinel-1 and Sentinel-2 data to a crop classification workflow.
Requirements
- Basic Python skills, including variables, functions, and working with notebooks.
- Familiarity with introductory machine learning concepts.
- A computer with internet access and a Google account for Google Colab.
- Access to Google Earth Engine for the lessons that use it.
Description
Learn to analyze satellite imagery with Python and deep learning through practical geospatial workflows. This course connects imagery preprocessing, convolutional neural networks (CNNs), and semantic segmentation, helping you understand how data preparation, model design, and evaluation fit together.
Start by setting up your working environment with Google Colab and exploring TensorFlow and PyTorch. Then work through satellite imagery preprocessing, geospatial indices, zonal statistics, and Google Earth Engine data pipelines. Lessons cover Sentinel-2 imagery and a crop classification workflow that combines Sentinel-1 and Sentinel-2 data.
Next, explore CNNs for satellite image classification and crop health analysis. Learn to assess model performance using accuracy, precision, recall, and cross-validation, and explore hyperparameter tuning with grid search and random search. Applied lessons also introduce plant counting and biomass prediction with ground-truth validation.
A dedicated section takes you through semantic segmentation with U-Net: understanding the architecture, building the model from scratch, training and evaluating it, visualizing predictions, and exporting results as GeoTIFF files.
The course is designed for GIS professionals, researchers, students, and data scientists who have basic Python skills and familiarity with introductory machine learning. Quizzes help reinforce the concepts as you progress.
By the end, you will have practiced workflows for preparing satellite imagery, building classification and segmentation models, evaluating predictions, and producing geospatial outputs. Use these foundations to develop your own experiments with new datasets and study areas.
Author(s): Senior Assist Prof Azad Rasul
