Satellite Imagery Supervised Learning Sequence

Supervised learning is a type of machine learning where algorithms learn from a labelled dataset. In the context of satellite imagery, this technique is used for tasks like identifying specific objects (e.g., rooftops, roads, types of vegetation) or classifying land cover.

The question asks for the correct sequence of steps involved in applying supervised learning to satellite imagery analysis. Let's break down the process and the given steps:

Understanding the Steps:

• D. Identify target objects like rooftops: This step often represents the initial phase where the specific goal or objective of the analysis is defined. What are we looking for in the satellite images? For example, identifying all residential rooftops.
• A. Input labelled training data-set: Once the target objects are identified, the next crucial step is to gather and prepare the data. This involves collecting satellite images and manually labelling (annotating) the areas that contain the target objects. This labelled data acts as the ground truth for the model.
• B. Train machine learning model to organise features: With the labelled training dataset ready, the machine learning model is trained. During training, the model learns to recognize the patterns, textures, shapes, and other features associated with the target objects based on the provided labels. It essentially learns to 'organise' or map these features to the correct object class.
• C. Classify rest of image: After the model is trained and validated, it can be used to analyze new, unseen satellite images. This final step involves applying the trained model to the entire image (or large portions of it) to automatically identify and classify the target objects across the broader area.

Correct Sequence Explanation:

Based on the typical workflow and the interpretation of the steps:

1. Identify target objects like rooftops (D): First, define what you need to find.
2. Input labelled training data-set (A): Then, create or obtain data where these objects are already labelled.
3. Train machine learning model to organise features (B): Use this labelled data to teach the model.
4. Classify rest of image (C): Finally, use the trained model to find these objects in new images.

Therefore, the correct sequence is D, A, B, C.

Applying the Sequence:

Imagine you want to map all solar panels in a city using satellite images.

• D. Identify target objects: The target object is 'solar panels'.
• A. Input labelled training data-set: You collect high-resolution satellite images of different areas and manually draw boxes around known solar panels, creating a dataset where each panel location is labelled.
• B. Train machine learning model: You feed these labelled images into a model (like a Convolutional Neural Network) which learns the visual characteristics (shape, texture, context) of solar panels.
• C. Classify rest of image: You then run the trained model on new satellite images of the entire city to automatically detect and map all locations likely to contain solar panels.