Processing and Analyzing Satellite Imagery with AWS Lambda and S3

In this hands-on guide, we will explore how to ingest, process, and analyze satellite imagery using AWS Lambda functions and S3 storage. Satellite imagery is a rich source of data for various applications, such as urban planning, environmental monitoring, and agriculture. With the power of AWS Lambda and S3, we can create a serverless architecture to handle large-scale satellite imagery processing and analysis.

Prerequisites

• An AWS account with appropriate permissions to create and manage AWS Lambda functions and S3 buckets
• Familiarity with AWS services like Lambda and S3
• Basic understanding of Python programming
• Knowledge of remote sensing and satellite imagery concepts

Step 1: Setting up an S3 Bucket for Satellite Imagery Storage

1. Sign in to the AWS Management Console and navigate to the S3 service.
2. Click on "Create bucket" and provide a unique name for your bucket.
3. Choose a region where you want to store your satellite imagery data.
4. Configure any additional settings as needed, and click "Create bucket".

Step 2: Uploading Satellite Imagery to S3

1. Download sample satellite imagery (e.g., Landsat or Sentinel) in GeoTIFF format.
2. Navigate to the S3 bucket you created in Step 1.
3. Click on "Upload" and select the downloaded satellite imagery.
4. Complete the upload process by clicking "Upload" again.

Step 3: Creating a Lambda Function for Satellite Imagery Processing

1. Navigate to the AWS Lambda service in the AWS Management Console.
2. Click on "Create function" and choose "Author from scratch".
3. Provide a name for your Lambda function and choose Python as the runtime environment.
4. Create or choose an existing execution role with permissions to access S3 and other required services.
5. Click on "Create function".

Step 4: Installing Dependencies and Writing the Lambda Function Code

1. Install the required Python libraries for processing satellite imagery, such as Rasterio and NumPy. You can create a requirements.txt file and list the necessary packages:
   rasterio
   numpy
   boto3

2. Use the following command to create a Lambda deployment package that includes the dependencies:
   pip install -r requirements.txt -t package/
   cp lambda_function.py package/
   cd package
   zip -r ../deployment_package.zip .
   cd ..

3. Upload the deployment_package.zip to your Lambda function by navigating to the "Function code" section in the Lambda console and choosing "Upload a .zip file" from the "Code entry type" dropdown.

4. Write the code for your Lambda function to process the satellite imagery. The code should read the satellite image from the S3 bucket, perform the desired processing (e.g., calculating NDVI, extracting spectral bands), and save the results back to S3.

import boto3
import rasterio
import numpy as np
from io import BytesIO

s3 = boto3.client('s3')

def lambda_handler(event, context):
    # Replace with the name of your S3 bucket and the input image
    bucket_name = 'your-bucket-name'
    input_image_key = 'path/to/input/image.tif'
    output_image_key = 'path/to/output/image.tif'

    # Read the satellite image from S3
    input_image_object = s3.get_object(Bucket=bucket_name, Key=input_image_key)
    input_image_data = input_image_object['Body'].read()

    # Open the image with Rasterio
    with rasterio.open(BytesIO(input_image_data)) as src:
       # Perform the desired processing on the satellite image
       # For example, calculating NDVI or extracting spectral bands
       # ...

       # Save the processed image data to a BytesIO object
       output_image_data = BytesIO()
       with rasterio.open(output_image_data, 'w', **src.profile) as dst:
           # Write the processed data to the output image
           # ...

    # Save the processed image back to S3
    output_image_data.seek(0)
    s3.put_object(Bucket=bucket_name, Key=output_image_key, Body=output_image_data)

5. Save the Lambda function code and adjust the function's timeout and memory settings as needed in the "Basic settings" section of the Lambda console.

Step 5: Triggering the Lambda Function

In the AWS Lambda console, navigate to the "Add trigger" section.
Choose the "S3" service as the trigger source.
Configure the trigger settings, such as selecting the S3 bucket created in Step 1 and specifying the event type (e.g., "Object Created").
Click on "Add" to finalize the trigger configuration.
Now, whenever a new satellite image is uploaded to the specified S3 bucket, the Lambda function will automatically process the image and save the results back to S3.

Conclusion

In this guide, we demonstrated how to ingest, process, and analyze satellite imagery using AWS Lambda and S3. With this serverless architecture, you can efficiently handle large-scale satellite imagery processing and analysis tasks without worrying about infrastructure management. You can further extend this solution to incorporate additional processing steps, machine learning models, or visualization tools to suit your specific use case.