Predict just about anything with Google Earth Engine. Part I

🌏 Predict Just About Anything with Google Earth Engine. Part I 🚀

Course Headline:

Sampling Strategies and Tools: The Essential Precursor to AI and Machine Learning

Course Description:

Key Takeaways:

• Cloud & Shadows Filtering: Master techniques to clean your datasets from cloud and shadow interference.
• Filtering a Collection: Learn to filter large image collections efficiently.
• Mapping Functions over Collections: Understand how to apply functions across a collection of images or data.
• Applying Masks to Images: Discover how to effectively use masks to isolate specific areas within images.
• Composite RGB Images: Create composite images for better visualization and analysis.
• Land Cover Land Use (LCLU) Classification: Gain expertise in classifying land cover and land use, a crucial step for any environmental study.
• Stratified & Balanced Sampling Strategies: Learn to sample data in a way that ensures representativeness and balance.
• Splitting Training/Validation Datasets: Understand the importance of splitting your dataset into training and validation sets for better model performance.
• Results Visualization: Visually communicate your findings with clear and effective visualizations.
• Classification Accuracy Assessment: Assess the accuracy of your classifications to ensure data reliability.
• Area Computation in Hectares: Calculate land area measurements for different LCLU classes.
• Exporting Data as Assets: Efficiently export your LCLU raster and vector data for future use.
• Time Series Aggregation: Build time series datasets, aggregating spatially and over time.
• SAR Data Processing (Linear to Decibel Conversion): Learn to process Sentinel 1 Synthetic Aperture Radar (SAR) data for various applications.
• Soil Moisture Inference: Calculate soil moisture levels using SAR data.
• NDVI Inference via Regression: Estimate vegetation health by inferring the Normalized Difference Vegetation Index (NDVI).
• Long Term Statistic Computation: Analyze long-term trends in your data using balanced samples.
• Pixel Performance Analysis: Identify and address low or high performing pixels to improve data quality.
• Covariate Screening for Predictive Use: Select the most predictive covariates for your models.
• Advanced Time Series Data Creation & Aggregation: Generate complex time series data, aggregating information from various datasets over different time frames.
• Data Aggregation at Different Granules: Combine data from different sources at the granule of your choice.
• Creating Meaningful Parcels/Paddocks/Pixels: Design parcels or pixels for meaningful data aggregation and analysis.
• Exporting Data as .CSV: Easily export your processed data into CSV files for further analysis or reporting.

By the end of this course, you'll be equipped with a comprehensive set of tools and knowledge to not only predict just about anything but also ensure that your predictions are robust, accurate, and representative of the real world. Join us on this transformative learning journey with Google Earth Engine! 📊🌱

Enroll Now and Predict Your Future Success! 🎓💫