Using AI and Machine Learning for Advanced Crop Mapping

This section guides you through the process of training machine learning models for land cover classification and crop mapping. You’ll learn the complete model training workflow through detailed slides and engage in practical exercises using Google Colab and Vertex AI. These exercises cover training models, creating inferences, and deploying fitted models for real-world environmental analysis. By the end, you’ll have both the theoretical understanding and hands-on experience needed to develop and deploy effective machine learning solutions for crop mapping and environmental sustainability.

Begin with an in-depth presentation that outlines the entire workflow of model training, covering best practices, strategies, and key considerations in developing robust machine learning models. The hands-on exercises must be completed in order, as they build on each other.

Practical Exercises:

1. Training Models in Google Colab: Dive into hands-on model training within the collaborative environment of Google Colab, experimenting with various model architectures.
2. Creating inference in Google Colab: Use a model to make predictions on new imagery.
3. Training Models: Elevate your model training by utilizing Vertex AI, Google’s advanced machine learning platform, to efficiently train models at scale.
4. Deploying a Fitted Model: Master the art of deploying your trained models, making them accessible for real-world environmental analysis and predictions.