Biomass Prediction Model

Overview

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)
Task: Regression (Biomass prediction in Mg/ha)
Input: Multi-spectral satellite imagery (GeoTIFF format)
Output: Biomass heatmap and statistics
Creator: vertify.earth
Partner: GIZ Forest Forward initiative
Date: May 16, 2025

Key Features

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

import requests
import io
from PIL import Image

# API endpoint
API_URL = "https://api-inference.huggingface.co/models/vertify/biomass-prediction"
headers = {"Authorization": f"Bearer {API_TOKEN}"}

def predict_biomass(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

# Example usage
result = predict_biomass("path/to/your/satellite_image.tif")

Local Installation

# Clone the repository
git clone https://huggingface.co/vertify/biomass-prediction
cd biomass-prediction

# Install dependencies
pip install -r requirements.txt

# Run the Gradio app
python 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:

# Example usage
python 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
Radar bands: Sentinel-1 VV, VH polarizations (if available)
DEM: Digital Elevation Model (if available)
Format: GeoTIFF with proper georeferencing

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:

HuggingFace: @vertify
GitHub: vertify
Email: [email protected]

vertify
/

biomass-model