AI Platform Model Definition For Earth Engine: A Guide

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Hey guys! Are you looking to leverage the power of Deep Neural Networks (DNNs) within Google Earth Engine? You've come to the right place! Integrating models hosted on Google AI Platform with Earth Engine can be a game-changer for various geospatial applications. But, like many of you, you might be facing some challenges in configuring the input-output definitions correctly. Don't worry, we've got you covered. This article dives deep into Google AI Platform model definition for seamless Earth Engine integration. We'll break down the complexities, address common issues, and provide a step-by-step guide to get your models up and running.

The core challenge lies in bridging the gap between Earth Engine's data structures and the input requirements of your AI Platform model. Earth Engine primarily deals with imagery and feature collections, often represented as multi-dimensional arrays or GeoJSON features. On the other hand, AI Platform models typically expect numerical inputs in a specific format, such as tensors. So, how do we effectively translate Earth Engine data into a format that your AI Platform model can understand and, more importantly, how do we interpret the model's output back within Earth Engine?

This process involves carefully defining the input and output schemas of your AI Platform model. These schemas act as a contract, specifying the data types, shapes, and names of the inputs and outputs. A mismatch in these definitions can lead to errors during prediction, preventing you from harnessing the full potential of your model within Earth Engine. Let's explore the key steps and considerations for defining these schemas correctly.

Before we dive into the practical aspects, let's clarify some fundamental concepts. When deploying a model to AI Platform for Earth Engine use, you need to consider:

  • Input Data Format: Your model's input definition must align with the format of the data you're sending from Earth Engine. This often involves converting Earth Engine images or feature properties into numerical arrays or lists.
  • Output Data Format: Similarly, the model's output definition should be compatible with how you intend to use the results within Earth Engine. This might involve converting the model's predictions (e.g., class probabilities) back into Earth Engine images or feature properties.
  • Serving Function: The serving function acts as the interface between AI Platform and Earth Engine. It preprocesses the input data, feeds it to the model, and post-processes the output before returning it to Earth Engine. The serving function is crucial for handling data type conversions and reshaping operations.
  • Model Signature: The model signature defines the inputs and outputs of your model. It's a critical part of the deployment process, as it tells AI Platform how to interact with your model.

Now, let's get into the nitty-gritty of defining these schemas.

Let's break down the process of defining input-output schemas for your AI Platform model in a step-by-step manner:

1. Understanding Your Model's Requirements

First and foremost, you need to have a crystal-clear understanding of your model's input and output expectations. This involves knowing:

  • Input Features: What are the specific features (e.g., spectral bands, indices, elevation) that your model requires?
  • Input Data Types: What data types (e.g., float32, int64) does your model expect for each feature?
  • Input Shape: What is the expected shape (e.g., [1, 256, 256, 3] for a 256x256 RGB image) of the input tensor?
  • Output Predictions: What type of predictions does your model generate (e.g., class probabilities, regression values)?
  • Output Data Types: What data types are the predictions in?
  • Output Shape: What is the shape of the output tensor?

This information is crucial for defining the input and output signatures of your model.

2. Preprocessing Data in Earth Engine

Before sending data to your AI Platform model, you'll likely need to preprocess it within Earth Engine. This might involve:

  • Selecting Features: Choosing the specific bands or properties that your model needs.
  • Scaling or Normalizing: Scaling the pixel values or feature properties to a specific range (e.g., 0-1) or normalizing them to have zero mean and unit variance. This is crucial for optimal model performance.
  • Reshaping Data: Reshaping the data into a format that your model expects. For example, you might need to convert an Earth Engine image into a 1D array or a 2D array.

You can use Earth Engine's built-in functions like ee.Image.toArray() and ee.Image.sample() to prepare your data. The key is to ensure that the output of your Earth Engine preprocessing steps matches the expected input format of your AI Platform model.

3. Defining the Serving Function

The serving function is the heart of the integration between AI Platform and Earth Engine. It's the Python function that handles the input and output data transformations. Here's a breakdown of what a serving function typically does:

  • Input Processing: It receives the input data from Earth Engine (usually as a JSON object). It then needs to parse this data and convert it into a format that your model can understand (e.g., a NumPy array).
  • Model Prediction: It calls your model's predict() method with the processed input data.
  • Output Post-processing: It receives the model's predictions and transforms them into a format that Earth Engine can handle (e.g., an Earth Engine image or feature property).

Let's look at an example of a serving function:

import tensorflow as tf
import numpy as np

def serving_function():
    # Define input signature
    inputs = {
        'image': tf.io.FixedLenFeature(shape=(256, 256, 3), dtype=tf.float32)
    }

    # Define output signature
    outputs = {
        'predictions': tf.io.FixedLenFeature(shape=(10,), dtype=tf.float32) # Example: 10 class probabilities
    }

    @tf.function(input_signature=[tf.TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name='image')])
    def serving_fn(image):
        # Load your model here (e.g., from a SavedModel)
        model = tf.keras.models.load_model('path/to/your/model')
        predictions = model(image)
        return {'predictions': predictions}

    return serving_fn.get_concrete_function(tf.TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name='image'))

Key takeaways from the example:

  • We use tf.io.FixedLenFeature to define the input and output schemas. The shape and dtype parameters are crucial for specifying the data type and dimensions.
  • The @tf.function decorator compiles the serving function into a TensorFlow graph, which improves performance.
  • We load the model using tf.keras.models.load_model(). Replace 'path/to/your/model' with the actual path to your saved model.
  • The serving_fn function takes the input tensor, feeds it to the model, and returns the predictions.
  • The get_concrete_function method creates a concrete function with a specific input signature, which is required for AI Platform deployment.

4. Defining the Model Signature

The model signature is a crucial part of deploying your model to AI Platform. It tells AI Platform what inputs your model expects and what outputs it will produce. You can define the model signature in your serving function or when you save your model.

Here's how you can define the model signature when saving your model using TensorFlow:

import tensorflow as tf

# Assuming you have a trained Keras model
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Define input signature
input_signature = [
    tf.TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name='image')
]

# Get the serving function
@tf.function(input_signature=input_signature)
def serve(image):
    return model(image)

concrete_function = serve.get_concrete_function(*input_signature)

# Save the model with the serving function
model.save('path/to/your/model', save_format='tf', signatures=concrete_function)

In this example, we define the input signature using tf.TensorSpec. The shape parameter specifies the shape of the input tensor (in this case, a batch of 256x256 RGB images), and the dtype parameter specifies the data type (float32). The name parameter is optional but recommended, as it helps in identifying the input tensor later.

5. Deploying the Model to AI Platform

Once you have your serving function and model signature defined, you can deploy your model to AI Platform. This involves:

  • Creating a Model: Creating a model resource in AI Platform.
  • Creating a Version: Creating a version of your model and specifying the path to your saved model.
  • Specifying the Serving Function: If you defined the serving function separately, you'll need to specify its path when creating the version.

You can deploy your model using the gcloud command-line tool or the Google Cloud Console. Refer to the AI Platform documentation for detailed instructions.

6. Making Predictions from Earth Engine

After deploying your model, you can make predictions from Earth Engine using the ee.Model.fromAiPlatformPredictor() function. This function takes the name of your AI Platform model and version, along with the input and output signatures.

Here's an example of how to make predictions from Earth Engine:

import ee
ee.Initialize()

# Define the AI Platform model name and version
MODEL_NAME = 'your-model-name'
MODEL_VERSION = 'your-model-version'

# Define the input and output signatures
INPUT_NAME = 'image'
OUTPUT_NAME = 'predictions'

# Create the AI Platform predictor
predictor = ee.Model.fromAiPlatformPredictor(
    projectName='your-project-id',
    modelName=MODEL_NAME,
    versionName=MODEL_VERSION,
    inputImageProperties=[INPUT_NAME],
    outputImageProperties=[OUTPUT_NAME]
)

# Load an Earth Engine image
image = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').first().select(['B4', 'B3', 'B2']).toFloat().divide(10000)

# Reshape the image to match the model's input shape
image = image.toArray().toArray(1)

# Make a prediction
prediction = predictor.predict(image.rename([INPUT_NAME]))

# Get the prediction as an image
prediction_image = prediction.select(OUTPUT_NAME).arrayFlatten([['class_probability']])

# Print the prediction
print(prediction_image.getInfo())

Key points in this example:

  • We use ee.Model.fromAiPlatformPredictor() to create a predictor object.
  • We specify the project ID, model name, and version.
  • We define the inputImageProperties and outputImageProperties parameters, which tell Earth Engine which image properties to use as input and output.
  • We load an Earth Engine image and preprocess it to match the model's input shape.
  • We call the predict() method on the predictor object to make a prediction.
  • We extract the prediction as an Earth Engine image and print its information.

Integrating AI Platform models with Earth Engine can sometimes be tricky. Here are some common issues and their solutions:

  • Input Shape Mismatch: This is a very common issue. Ensure that the shape of the input data you're sending from Earth Engine matches the expected input shape of your model. Use Earth Engine's array operations (e.g., toArray(), toArray(1)) to reshape your data.
  • Data Type Mismatch: Another frequent issue. Make sure the data types of your input features match the expected data types of your model. For example, if your model expects float32, ensure that you're sending float32 data from Earth Engine.
  • Serving Function Errors: Errors in the serving function can prevent your model from making predictions. Carefully debug your serving function and ensure that it correctly processes the input data and post-processes the output.
  • Model Signature Mismatch: If your model signature is incorrect, AI Platform might not be able to interact with your model. Double-check your model signature and ensure that it accurately reflects the inputs and outputs of your model.
  • Permissions Issues: You might encounter permission issues when trying to access your AI Platform model from Earth Engine. Ensure that your Earth Engine service account has the necessary permissions to access your AI Platform model.

Here are some best practices and tips for successful AI Platform and Earth Engine integration:

  • Thoroughly Test Your Model: Before deploying your model, test it thoroughly with various inputs to ensure that it's working correctly.
  • Use a Separate Environment: Develop and test your serving function in a separate environment (e.g., a Jupyter notebook) before deploying it to AI Platform.
  • Log Your Predictions: Log your model's predictions to monitor its performance and identify any issues.
  • Version Your Models: Use model versioning to track changes and easily roll back to previous versions if needed.
  • Optimize Your Model: Optimize your model for performance to reduce prediction latency and costs.

Integrating Google AI Platform models with Earth Engine opens up a world of possibilities for geospatial analysis and applications. By carefully defining input-output schemas, creating robust serving functions, and following best practices, you can seamlessly leverage the power of DNNs within Earth Engine. Remember, the key is to understand your model's requirements, preprocess your data effectively, and define your model signature accurately.

So, go ahead and start building amazing geospatial applications with the power of AI! If you have any questions or run into any issues, feel free to ask in the comments below. Happy coding!

This article focuses on Google AI Platform model definition for Earth Engine usage. We discussed the importance of properly configuring input-output schemas, serving functions, and model signatures. We also covered common issues and best practices for successful integration. You should be able to define input-output schemas for AI Platform models and deploy them for use within Earth Engine. This includes understanding the Earth Engine data format and how to convert it to match the model's expectations. Earth Engine integration with AI Platform enables powerful geospatial analysis using deep learning models.