AI.Geolabs · Premium GeoAI course
Forest Biomass Modeling
Ready to move beyond classical machine learning? Learn how to build and evaluate neural networks for forest biomass estimation.
Connect GEDI LiDAR biomass observations with Sentinel-2 predictors. Start with an artificial neural network, then explore a 1D convolutional neural network for modelling aboveground biomass density.
Build the skills to assess these approaches for your biomass work, with a clear understanding of what the data and evaluation can support.
GEDI LiDAR · Sentinel-2 · Artificial neural networks · 1D CNNs
The challenge behind biomass estimation
Changing the model does not remove the data problem.
You already know how to fit a classical ML model. Moving to neural networks raises a different question. How do you make the added complexity useful?
Forest biomass is not measured directly by optical imagery. You need reference observations and predictors that capture relevant relationships. GEDI provides footprint-level biomass estimates, while Sentinel-2 provides image-derived predictors. Preparing those sources for training is a modelling decision in its own right.
“How do I build the training data?”
Biomass observations and image predictors come from different measurements. Pairing them thoughtfully is the foundation of a useful training dataset.
“Where do I start with neural networks?”
Layers and training settings can quickly become a list of unfamiliar choices. A baseline ANN gives you a practical starting point.
“Is a deeper model actually useful?”
More layers do not guarantee better estimates. You need to evaluate predictions before drawing conclusions about model performance.
What you will gain
Add neural network modelling to your forestry toolkit.
Move from familiarity with classical ML to hands-on experience preparing data, training neural networks, and evaluating biomass predictions.
Prepare the foundation
Connect GEDI biomass with Sentinel-2 predictors.
Create AGBD training data by pairing GEDI footprint-level biomass estimates with Sentinel-2 predictors.
Why it matters: You practice the link between reference observations and predictor data, instead of beginning with an unexplained training table.
Start with an ANN
Build your first neural network baseline for AGBD.
The course introduces artificial neural networks. Build, train, and evaluate a baseline ANN to model relationships between the input predictors and biomass density.
Why it matters: You gain a starting point for understanding neural network regression before moving to convolutional layers.
Explore convolution
Use a 1D CNN to learn spectral patterns.
Explore how one-dimensional convolutional layers can learn patterns across an ordered spectral input representation for AGBD modelling.
Why it matters: You add another modelling approach and learn why input structure matters when using convolution.
Assess the result
Judge predictions before trusting model complexity.
Practice evaluating a neural network baseline and interpreting biomass predictions in relation to the training data and modelling choices.
Why it matters: You develop a basis for assessing whether a neural network is useful for your task. Better performance remains a question to test.
Beyond a generated neural network script
Getting a network to run is only part of biomass modelling.
An AI assistant or tutorial can provide an ANN or CNN implementation. That does not settle how to prepare your GEDI and Sentinel-2 data, whether the inputs suit the architecture, or how to interpret the predictions.
This course connects those steps around a specific forestry task: estimating aboveground biomass density. You start with training data preparation, learn through a baseline ANN, and then explore 1D convolution.
The value is in understanding the approach well enough to assess it, rather than treating deep learning as an automatic upgrade.
From “I know classical ML” to “I can prepare biomass training data, build a neural network, and evaluate its predictions.”
ANNs and CNNs expand your modelling options. Their usefulness depends on the data, the task, and the evidence from evaluation.
What the hands-on work develops
A practical introduction to neural networks for AGBD.
Work through the relationship between training data, model architecture, and predicted biomass density.
What you will build
- AGBD training data pairing GEDI biomass estimates with Sentinel-2 predictors.
- A baseline artificial neural network for AGBD regression.
- A 1D convolutional neural network for AGBD modelling.
What you will learn to assess
- How reference biomass and image predictors form a training dataset.
- How an ANN learns nonlinear relationships from its inputs.
- How convolutional layers learn patterns in a spectral representation.
- What model evaluation supports about the predictions.
GEDI biomass values are footprint-level estimates, not error-free reference measurements. This course models AGBD. Carbon accounting, uncertainty quantification, and verification require additional methods and evidence.
Is this course for you?
For forestry and remote sensing professionals ready to explore deep learning.
This course is for people who already know classical machine learning and want to add neural network approaches to their biomass and carbon work.
It is particularly relevant if you understand regression but want a practical introduction to ANNs, followed by an example of 1D convolution for biomass modelling.
The focus is AGBD estimation using GEDI and Sentinel-2, with an emphasis on understanding the models you build.
What you will need
- Basic Python.
- Basic machine learning knowledge.
- Access to Google Earth Engine.
The course introduces artificial neural networks, so prior experience building an ANN is not a stated prerequisite.
Course coverage
Training data, a neural network baseline, and 1D convolution.
Three connected areas of hands-on work build on your existing machine learning knowledge.
Creating AGBD Training Data with GEDI and Sentinel-2
Pair GEDI footprint-level biomass estimates with Sentinel-2 predictors to create the training data used for AGBD modelling.
Artificial Neural Network (ANN) for Modeling AGBD
Learn the ANN approach through building, training, and evaluating a baseline neural network for aboveground biomass density estimation.
1D Convolutional Neural Network (CNN) for Modeling AGBD
Use one-dimensional convolutional layers to learn spectral patterns for AGBD modelling.
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A few practical questions
Do I need previous deep learning experience?
The course introduces artificial neural networks. The stated requirements are basic Python, basic machine learning, and Google Earth Engine. It is intended for learners who already know classical ML.
Will neural networks outperform my classical ML model?
That is not guaranteed. Performance depends on the training data, modelling choices, and evaluation. The course adds ANN and 1D CNN approaches to your toolkit; any improvement needs to be demonstrated.
What data will I use?
You will pair GEDI footprint-level biomass estimates with Sentinel-2 predictors to build AGBD training data.
What does the 1D CNN learn?
The course uses one-dimensional convolutional layers to learn spectral patterns. The ordering and representation of inputs matter because convolution operates over neighbouring positions in that representation.
Does the course cover carbon accounting or verification?
The stated course scope is aboveground biomass density modelling. AGBD can inform carbon work, but carbon conversion, uncertainty analysis, and verification require additional methods beyond the activities listed here.
Where can I check pricing and access?
Visit the AI.Geolabs membership plans page for current pricing, course availability, and access terms.