1D CNN for Forest Biomass Modeling: Build a Deep Learning Model in Python (Lab 2c)

Summary
In this tutorial, we dive into modeling aboveground biomass density (AGBD) using a 1D convolutional neural network (1D CNN) in Python and Google Colab!

You will learn how to:
- Install and import essential geospatial and machine learning libraries.
- Load Sentinel-2 imagery and visualize it.
- Prepare features (spectral bands and vegetation indices) and AGBD labels.
- Scale, reshape, and split the data for 1D CNN training.
- Set up a workflow to predict biomass density across a landscape.

We use Sentinel-2 predictors, GEDI Level 4A data, and 1D CNN to build an efficient AGBD model. This hands-on exercise provides a foundation for applying deep learning to geospatial datasets.

Key Libraries: rasterio, earthpy, TensorFlow, Scikit-Learn

1. Python code

2. Access courses at Ai. Geelabs

3. Buy 'Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach' book