# Deep Learning for Geospatial Science Workshop **Duration: 2 hours** ## Workshop Overview This hands-on workshop introduces deep learning concepts and applications in geospatial science, focusing on practical implementations using popular frameworks and tools. ## Prerequisites - Basic Python programming knowledge - Familiarity with GIS concepts - Laptop with Python environment (preferably Anaconda) - Basic understanding of machine learning concepts - Required packages: `pytorch`, `TensorFlow`, `torchgeo`, `arcgis.learn`, `gdal`, `rasterio`, `geopandas` ## Schedule ### Part 1: Introduction to GeoAI (20 minutes) #### What is GeoAI? - Integration of artificial intelligence with geospatial data - Enhancing spatial analysis with machine learning - Applications in various sectors like environmental monitoring, urban planning, and disaster management #### Deep Learning in Geospatial Context - Difference between traditional machine learning and deep learning - Benefits of deep learning for complex spatial patterns - Common neural network architectures used in geospatial tasks ### Part 2: Tools and Frameworks Overview (20 minutes) #### ArcGIS API for Python and `arcgis.learn` - High-level API for GIS and deep learning tasks - Pre-trained models and transfer learning - Integration with ArcGIS Online and Pro #### TorchGeo - Datasets and samplers for geospatial data - Integration with PyTorch - Customizable data loaders for satellite imagery #### Other Essential Libraries - `GDAL` and `rasterio` for raster data handling - `geopandas` for vector data manipulation - `TensorFlow` for deep learning model development

# Install necessary packages (if not already installed)
!pip install torch torchvision torchgeo arcgis gdal rasterio geopandas

Part 3: Hands-on Example - Land Cover Classification

In this section, we will perform land cover classification using satellite imagery. We will use torchgeo and arcgis.learn to build, train, and evaluate our models.

Dataset Preparation

We will use a sample dataset containing satellite images and their corresponding land cover labels.

# Import necessary libraries
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import transforms
from torchgeo.datasets import EuroSAT
from torchgeo.transforms import indices

import matplotlib.pyplot as plt
import numpy as np

Load the Dataset

We will use the EuroSAT dataset, which contains Sentinel-2 satellite images covering 10 classes.

# Define transformations
transform = transforms.Compose([
    transforms.Resize((64, 64)),
    transforms.ToTensor()
])

# Load the dataset
dataset = EuroSAT(root="data/EuroSAT", split="train", transforms=transform, download=True)

# Split into training and validation sets
train_size = int(0.8 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])

# Create data loaders
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)

Define the Model

We will use a simple convolutional neural network for classification.

# Define the model
class SimpleCNN(nn.Module):
    def __init__(self, num_classes):
        super(SimpleCNN, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(13, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(64 * 16 * 16, 256),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(256, num_classes)
        )
    
    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x)
        return x

num_classes = 10
model = SimpleCNN(num_classes)

Training the Model

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Training loop
num_epochs = 5
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    
    # Validation
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in val_loader:
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    
    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader):.4f}, Validation Accuracy: {100 * correct / total:.2f}%")

Visualize Sample Predictions

# Get a batch of validation data
dataiter = iter(val_loader)
images, labels = dataiter.next()

# Get predictions
outputs = model(images)
_, predicted = torch.max(outputs, 1)

# Display images and predictions
fig = plt.figure(figsize=(12, 8))
for idx in range(8):
    ax = fig.add_subplot(2, 4, idx+1)
    img = images[idx].numpy().transpose((1, 2, 0))
    img = np.clip(img, 0, 1)
    ax.imshow(img)
    ax.set_title(f"Predicted: {dataset.classes[predicted[idx]]}\nActual: {dataset.classes[labels[idx]]}")
    ax.axis('off')
plt.show()

Part 4: Using arcgis.learn for Object Detection

We will now use arcgis.learn to perform object detection on geospatial data.

# Import necessary libraries
from arcgis.gis import GIS
from arcgis.learn import SingleShotDetector, prepare_data

# Connect to GIS (anonymous)
gis = GIS()

# Prepare the data
data_path = 'path_to_your_data'
data = prepare_data(data_path, batch_size=8, chip_size=224)

# Initialize the model
model = SingleShotDetector(data)

# Train the model
model.fit(epochs=5, lr=0.001)

Evaluate the Model

# Show results
model.show_results()

Part 5: Best Practices and Optimization

Data Augmentation

Applying data augmentation techniques to improve model generalization.

# Define augmentations
augmentation = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomVerticalFlip(),
    transforms.RandomRotation(30)
])

# Apply augmentations in data loader
train_dataset.dataset.transform = transforms.Compose([augmentation, transform])

Hyperparameter Tuning

• Adjust learning rates
• Modify network architectures
• Experiment with batch sizes

Part 6: Q&A and Resources

Additional Resources

• ArcGIS API for Python Documentation
• TorchGeo Documentation
• PyTorch Tutorials
• Esri GeoAI Resources

Follow-up Learning Path

• Explore advanced models like UNet, ResNet, and DenseNet
• Dive into semantic segmentation tasks
• Learn about deploying models in production environments
• Engage with the geospatial data science community

References

[1] Esri. (2023). Deep Learning Models in ArcGIS Learn

[2] Esri. (2023). GeoAI Overview

[3] PyTorch. (2023). PyTorch Documentation

[4] TorchGeo. (2023). TorchGeo Documentation

Thank You!

Feel free to reach out if you have any questions or need further assistance with your geospatial deep learning projects.