WACAfricaModel

Commodity Crop Model Documentation

This document describes the land use model used for monitoring EUDR-related commodity crops using remote sensing.

Model Overview

The model is an Attention U-Net with fusion mechanisms, specifically designed for land use monitoring using multi-source satellite data (Sentinel-1 and Sentinel-2)and geographic information (latitude, longitude, and elevation).

Model Architecture

• Base Architecture: Attention U-Net with multi-input fusion
• Original Framework: TensorFlow 2.14.0

Hardware requirements

A standard computer with a minimum of 16GB RAM to support the in-memory operations.

Software requirements

• Python >= 3.9
• Mamba (recommended for environment management)
• CUDA-compatible GPU

Key Dependencies for local users

• Tensorflow and Keras: Deep learning framework
• Scikit-learn: Machine learning
• Numpy: Data manipulation
• Rasterio: I/O georeferencing
• Google Earth Engine: For satellite data and elevation data access
• Geemap: Data download
• Matplotlib: For plotting

You can run the model either locally or via Colab. Below I provide both options. With the Colab option, you can run the model prediction interactively, and step-by-step code is provided. Of course, you can adapt it to your own environment.

Installation on local machine

Create and activate the virtual environment and install the package as follows: Approximate install time is 1 hour

mamba create -n tf214_py39 python=3.9 tensorflow=2.14.0 onnx tf2onnx ipykernel -c conda-forge -y && mamba activate tf214_py39 && python -m ipykernel install --user --name=tf214_py39 --display-name="TF 2.14 + ONNX"

Then install these packages as well:

mamba install earthengine-api geemap rasterio numpy matplotlib ipywidgets onnxruntime requests folium pyproj tqdm -q

Running the model on an interactive notebook in Google Colab (Everything is set and ready to run)

Click the link below (Open In Colab) and follow the instructions to run the analysis interactively in Google Colab:

#The notebook allows you to:

🖼️ Draw or upload a Region of Interest (ROI) on an interactive map

🧠 Automatically selects AI model based on location (Africa, Southeast Asia, Latin America)

🛰️ Downloads and preprocesses Sentinel-1 + Sentinel-2 + elevation + indices

🌾 Predicts land use categories over deforested areas only using ONNX models

🗺️ Side-by-side map of RGB imagery + land use prediction

📤 Export predictions as GeoTIFF for GIS analysis

Model Input Specifications

The model expects a single input tensor with the following specifications:

• Input Name: "input"
• Shape: [1, 64, 64, 17]
  • Batch size: 1 (fixed)
  • Height: 64 pixels
  • Width: 64 pixels
  • Channels: 17 (combined features)
• Data Type: float32 (elem_type: 1)

Input Channel Organization

The 17 input channels are organized as follows:

1. Sentinel-2 Bands (Channels 0-8):

   • Blue, Green, Red
   • Red Edge 1, 2, 3
   • NIR
   • SWIR 1, 2
   • Note: These bands are normalized using log-transformation and percentile-based scaling

2. Radar Data (Channels 9-10):

   • VV polarization (normalized to [-25, 0] range)
   • VH polarization (normalized to [-30, -5] range)

3. Geographical Information (Channels 11-13):

   • Altitude (normalized to [-400, 8000] range)
   • Longitude (normalized to [-180, 180] range)
   • Latitude (normalized to [-60, 60] range)

4. Additional Features (Channel 14):

   • Derived indices (NDVI, EVI, NDRE)

Output Specifications

• Output Name: "activation_65"
• Shape: [1, 64, 64, 22]
  • Batch size: 1 (fixed)
  • Height: 64 pixels
  • Width: 64 pixels
  • Classes: 1 (probability distribution over crop type)
• Data Type: float32 (elem_type: 1)

Land use following deforestation classes

The model output is variable depending on the region. The model predicts 25, 22, and 24 land use types for Africa, Latin America, and Southeast Asia, respectively. Each pixel in the output contains a probability distribution over this class.

Example format for each pixel:

Africa
classes = [
    # Land use classes
    0: "Background", 1: "Other large-scale cropland", 2: "Pasture", 3:'Mining', 4:'Other small-scale cropland', 5:'Roads', 6:'Other land with tree cover/Regrowth', 7:'Plantation forest',
    8:'Coffee', 9:'Build_up', 10:'Water', 11:'Oil_palm', 12:'Rubber', 13:'Cocoa', 14:'Avocado', 15:'Soy', 16:'Sugar', 17:'Maize', 18:'Banana', 19:'Pineapple',
    20:'Rice', 21:'Wood_logging', 22:'Cashew', 23:'Tea', 24:'Others'

Latin America
classes = [
    # Land use classes
    0: "Background", 1: "Other large-scale cropland", 2: "Pasture", 3:'Mining', 4:'Other small-scale cropland', 5:'Roads', 6:'Other land with tree cover/Regrowth', 7:'Plantation forest',
    8:'Coffee', 9:'Build_up', 10:'Water', 11:'Oil_palm', 12:'Rubber', 13:'Cocoa', 14:'Avocado', 15:'Soy', 16:'Sugar', 17:'Maize', 18:'Banana', 19:'Pineapple',
    20:'Rice', 21:'Wood_logging'

Southeast Asia
classes = [
    # Land use classes
    0: "Background", 1: "Other large-scale cropland", 2: "Pasture", 3:'Mining', 4:'Other small-scale cropland', 5:'Roads', 6:'Other land with tree cover/Regrowth', 7:'Plantation forest',
    8:'Coffee', 9:'Build_up', 10:'Water', 11:'Oil_palm', 12:'Rubber', 13:'Cocoa', 14:'Clove', 15:'Soy', 16:'Sugar', 17:'Maize', 18:'Banana', 19:'Pineapple',
    20:'Rice', 21:'Wood_logging', 22:'Cashew', 23:'Tea'

]

Usage Example (Actual usage refers to interactive notebook)

import onnxruntime as ort
import numpy as np

# Load the ONNX model
session = ort.InferenceSession("Land_use_following_deforestation_model.onnx")

# Prepare input data (example)
input_data = np.random.rand(1, 64, 64, 17).astype(np.float32)

# Run inference
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
predictions = session.run([output_name], {input_name: input_data})[0]

# Get predicted class
predicted_classes = np.argmax(predictions, axis=-1)

Models

The models are found at:

• Africa: {Africa Model}
• Southeast Asia: {SEA Model}
• Latin America: {LA Model}

Deployment Considerations

1. The model requires normalized input data according to the specified ranges for each channel
2. Memory requirements: Approximately 150MB for model weights
3. Compatible with any ONNX Runtime supported platform
4. Recommended to use ONNX Runtime version 1.20.0 or higher

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ONNX Model Description

ONNX Model Description generated by [onnx.helper.printable_graph(onnx_model.graph)];

The model processes the 17 input channels by splitting them into three groups, each handled by a distinct pathway:

1. Channels 0–11 (12 channels): Processed by a U-Net-like structure for feature extraction and segmentation.
2. Channels 12–13 (2 channels): Handled by a second U-Net-like structure, mirroring the first.
3. Channels 14–16 (3 channels): Fed into a dense (fully connected) network to generate features, likely used as attention maps to guide the U-Nets.

These pathways are fused together, with attention mechanisms emphasizing key regions, to produce a final segmentation map. This design is ideal for tasks where input channels represent diverse information, such as optical, radar satellite imagery and location information.

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Components

The model relies on several standard deep learning components for image processing:

• Convolutional Layers: Extract spatial features using filters (e.g., 3x3 kernels), typically followed by batch normalization and ReLU activation for stability and efficiency.
• Max Pooling: Reduces feature map sizes (e.g., 64x64 to 32x32) during the encoder phase, capturing essential patterns while lowering computational load.
• Transpose Convolutions: Upsample feature maps in the decoder phase, restoring resolution (e.g., 16x16 to 32x32).
• Attention Mechanisms: Weight features based on attention maps from the dense network, focusing on critical areas.
• Concatenation: Merges features from different network parts, such as skip connections in U-Nets or the final fusion step.
• Sigmoid: Outputs class probabilities for the final segmentation map.

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Detailed Structure

Here’s a step-by-step walkthrough of how the model processes the input.

1. Input Splitting

The input tensor [1, 64, 64, 17] is divided along the channel dimension into 15 tensors of shape [1, 64, 64, 1], then grouped as:

• Channels 0–11: Concatenated into [1, 64, 64, 12].
• Channels 12–13: Concatenated into [1, 64, 64, 2].
• Channels 14–16: Concatenated into [1, 64, 64, 3].

Each group follows a unique processing path.

2. Dense Network (Channels 14–16)

• Input: [1, 64, 64, 3], transposed to [1, 3, 64, 64].
• Structure: Mirrors the U-Net for channels 10–11:
  • Encoder: Downsamples to [1, 512, 8, 8] through levels with 64, 128, 256, and 512 filters.
  • Decoder: Upsamples back to [1, 64, 64, 64] with attention and skip connections.
• Output: [1, 64, 64, 64] feature map.
• Purpose: Provides an attention signal or context for the U-Nets.

3. U-Net for Channels 12–13

• Input: [1, 64, 64, 2], transposed to [1, 2, 64, 64].
• Encoder (Downsampling):
  • Level 1: Two 3x3 conv layers (64 filters), output [1, 64, 64, 64], then max pooling to [1, 64, 32, 32].
  • Level 2: Two 3x3 conv layers (128 filters), output [1, 128, 32, 32], then max pooling to [1, 128, 16, 16].
  • Level 3: Two 3x3 conv layers (256 filters), output [1, 256, 16, 16], then max pooling to [1, 256, 8, 8].
  • Bottom: Two 3x3 conv layers (512 filters), output [1, 512, 8, 8].
• Decoder (Upsampling with Attention):
  • Level 3: Upsampled to [1, 256, 16, 16], combined with encoder features via skip connection and attention, processed with conv layers.
  • Level 2: Upsampled to [1, 128, 32, 32], attention applied, concatenated, and conv layers.
  • Level 1: Upsampled to [1, 64, 64, 64], attention applied, concatenated, and conv layers.
• Output: [1, 64, 64, 64] feature map.

4. U-Net for Channels 0–11

• Input: [1, 64, 64, 10], transposed to [1, 10, 64, 64].
• Structure: Mirrors the U-Net for channels 10–11:
  • Encoder: Downsamples to [1, 512, 8, 8] through levels with 64, 128, 256, and 512 filters.
  • Decoder: Upsamples back to [1, 64, 64, 64] with attention and skip connections.
• Output: [1, 64, 64, 64] feature map.

5. Fusion and Output

• Concatenation: Outputs from both U-Nets and the dense network (resized to [1, 256, 64, 64]) are combined into [1, 384, 64, 64] (64 + 64 + 256 = 384, though channel counts may vary).
• Final Convolution: A 1x1 conv layer with 22 filters produces [1, 22, 64, 64].
• Softmax: Generates class probabilities.
• Output: Transposed to [1, 64, 64, 1], a segmentation map with 1 class per pixel.

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Workflow Summary

1. Splitting: The 17-channel input is divided into three parts for specialized processing.
2. Feature Extraction:
   • U-Nets extract hierarchical features from channels 0–11 and 12–13 using encoder-decoder paths with skip connections.
   • The dense network processes channels 12–14 into a guiding feature map.
3. Attention: The dense network’s output weights U-Net features during upsampling, highlighting key areas.
4. Fusion: All features are combined to integrate multi-channel information.
5. Segmentation: A final layer classifies each pixel into one 1 class.

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Design Philosophy

This architecture is tailored for:

• Multi-Channel Inputs: Handles diverse data types across 17 channels.
• Attention-Driven Focus: Improves accuracy by emphasizing important regions.
• Detailed Segmentation: Preserves spatial details via U-Net skip connections.

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Summary

The Land use following deforestation model combines three U-Nets network to process a 17-channel, 64x64 input. Channels 0–11, 12–13 and 14 - 16 re handled by U-Nets for feature extraction, while channels 12–14 guide attention via a dense network. The fused output becomes a 64x64 segmentation map with 1 class for each pixel.

License

This work is licensed under the Apache License 2.0 - please see the LICENSE file for details.

Contact

For questions or issues, please open an issue on GitHub

Citation

Masolele et al., 2026