🏭 Coal Plant Predictions -- geospatial-classification

This model uses satellite data to predict if a coal plant is turned on and producing carbon emissions. The satellite data comes from Google Earth Engine.

• Model: 1D Fully Convolutional Network in TensorFlow
• Creating datasets: Sentinel-2 satellite data from Earth Engine
• Training the model: TensorFlow in Vertex AI
• Getting predictions: TensorFlow in Cloud Run

Creating a Google Cloud project

Before you begin, it is recommended to create a new Google Cloud project so that the activities from this lab do not interfere with other existing projects. You may refer to the official documentation (Creating and Managing Projects) for detailed instructions.

Running the notebooks

To run the notebooks successfully, follow the steps below.

Step 0: Select your Google Cloud project

Please make sure you select your own Google Cloud Project you can work on for this Coal Plant Predictions Lab.

Step 1: Create a User-Manged Notebook instance on Vertex AI Workbench

• Browse to Vertex AI Workbench page, Click on "USER-MANAGED NOTEBOOKS" and Click on "+ NEW NOTEBOOK" as shown in the image below.

• Please ensure you have selected the correct project when creating a new notebook. Upon clicking the "+ NEW NOTEBOOK", you will be presented with a list of notebook instance options. Select Customize..., this will give you Python version 3.10.

• Pick a name (or leave it default), select europe-west2 region and europ-west2-a zone, and then click "CREATE" to create the notebook instance.

• The instance will be ready when you see a green tick and can click on "OPEN JUPYTERLAB" on the User-Managed Notebooks page. It may take a few minutes for the instance to be ready.

Step 2: Open JupyterLab

• Click on "OPEN JUPYTERLAB", which should launch your Managed Notebook in a new tab.

Step 3: Opening a terminal

• Open a terminal via the file menu: File > New > Terminal

Step 4: Cloning the repo

• Run the following code to clone this repo: git clone https://github.com/teamdatatonic/geospatial-classification.git

• You can also navigate to the menu on the top left of the Jupyter Lab environment and click on Git > Clone a repository.

• Once cloned, you should now see the geospatial-classification folder in your main directory.

Step 5: Open the README.ipynb

• Follow the instructions in the notebook, and continue through the remaining notebooks.