This model predicts above-ground biomass (AGB) in forest ecosystems using multi-spectral satellite imagery. Developed by vertify.earth for the GIZ Forest Forward initiative, this tool supports sustainable forest management and carbon monitoring efforts. Biomass estimation is a critical component for carbon stock assessment, ecosystem monitoring, and sustainable forest management.
Model Details
Model Type: StableResNet (Custom PyTorch architecture)
Multi-source Fusion: Combines data from multiple satellite sensors (Sentinel-1, Sentinel-2, Landsat-8, PALSAR)
Advanced Feature Engineering: Calculates spectral indices, texture features, and spatial context features
Stable Architecture: Uses ResNet-inspired architecture with numerical stability improvements
Interactive Visualization: Provides heatmaps and RGB overlays of biomass predictions
Comprehensive Statistics: Calculates mean, median, min, max, and total biomass for the analyzed area
Performance
Metric
Value
R²
0.87
RMSE
28.7 Mg/ha
MAE
19.5 Mg/ha
Use Cases
Carbon Stock Assessment: Estimate carbon sequestration in forests
Biodiversity Monitoring: Monitor forest structure as a proxy for habitat quality
Sustainable Forestry: Plan and monitor sustainable timber harvesting
Land Use Change: Detect and quantify forest degradation and regrowth
Climate Change Research: Monitor changes in biomass over time
Usage
Gradio App
The easiest way to use this model is through the provided Gradio interface:
Upload a multi-band satellite image in GeoTIFF format
Select visualization type (heatmap or RGB overlay)
Click "Generate Biomass Prediction"
View the biomass map and statistics
API Usage
python
1import requests
2import io
3from PIL import Image
45# API endpoint6API_URL ="https://api-inference.huggingface.co/models/vertify/biomass-prediction"7headers ={"Authorization":f"Bearer {API_TOKEN}"}89defpredict_biomass(filename):10withopen(filename,"rb")as f:11 data = f.read()12 response = requests.post(API_URL, headers=headers, data=data)13return response.json()1415# Example usage16result = predict_biomass("path/to/your/satellite_image.tif")
Local Installation
bash
1# Clone the repository2git clone https://huggingface.co/vertify/biomass-prediction
3cd biomass-prediction
45# Install dependencies6pip install -r requirements.txt
78# Run the Gradio app9python app.py
Inference Script
This repository includes a full inference script (predict_biomass.py) that allows you to process satellite imagery in batch mode and generate biomass maps:
bash
1# Example usage2python predict_biomass.py --input_dir /path/to/satellite_images --output_dir /path/to/output --visualization_type heatmap
For full documentation on the inference script options, see the script header or run:
python predict_biomass.py --help
Full Training Pipeline
The complete training pipeline, including data preprocessing, feature engineering, model training, and evaluation is available in our GitHub repository. Please refer to the GitHub repository for detailed documentation on training your own biomass prediction models.
Input Data Requirements
For optimal results, your satellite imagery should include:
Optical bands: Blue, Green, Red, Near Infrared (NIR), SWIR1, SWIR2
The model has been trained on data from various forest types including tropical, temperate, and boreal forests, making it adaptable to different ecosystems.
Limitations
Performance may vary in extremely dense forests (>500 Mg/ha) due to saturation effects
Model accuracy depends on the quality and consistency of input satellite data
Cloud cover in optical imagery can reduce prediction quality
Limited validation in certain ecosystem types (e.g., mangroves, wetlands)
Citation
If you use this model in your research, please cite:
@misc{vertify2025biomass,
author = {vertify.earth},
title = {Biomass Prediction Model Using Multi-spectral Satellite Imagery},
year = {2025},
publisher = {HuggingFace},
note = {Developed for GIZ Forest Forward initiative},
howpublished = {\url{https://huggingface.co/spaces/vertify/biomass-prediction}}
}
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgements
Project developed by vertify.earth for the GIZ Forest Forward initiative
Training data sources include field measurements from various research institutions
Satellite imagery from ESA Copernicus Programme (Sentinel-1, Sentinel-2) and NASA/USGS (Landsat-8)
Special thanks to the open-source community for tools and libraries used in this project
Contact
For questions, feedback, or collaboration opportunities, please reach out via:
