I would use supervised classification for land cover mapping. This is because supervised classification allows me to train a model on a set of known land cover types, and then use that model to classify new images. This is a more accurate method than unsupervised classification, which simply groups pixels together based on their spectral similarity.

In supervised classification, I would first collect training samples for each land cover type I want to classify. These samples can be collected by manually selecting pixels in an image, or by using an automated method like random sampling. Once I have my training samples, I can train a classifier on them.

Deep learning is a powerful supervised approach to machine learning that can be used for land cover mapping. Deep learning models are trained on large datasets of labeled images, and they can learn to recognize patterns in the data that are not visible to humans. This makes them very accurate at classifying land cover types.

Here is a step-by-step process for using deep learning for land cover mapping:

Deep learning is a powerful tool for land cover mapping. It is accurate, scalable, and can be used to classify a wide variety of land cover types. However, it does require a large dataset of labeled images to train the model.

Here are some of the advantages of using deep learning for land cover mapping:

• High accuracy: Deep learning models can achieve very high accuracy at classifying land cover types.
• Scalability: Deep learning models can be scaled to large datasets. This makes them ideal for mapping large areas.
• Ability to learn complex patterns: Deep learning models can learn complex patterns in the data that are not visible to humans. This makes them very powerful at classifying land cover types.

Here are some of the disadvantages of using deep learning for land cover mapping:

• Requires a large dataset of labeled images: Deep learning models require a large dataset of labeled images to train. This can be time-consuming and expensive to collect.
• Can be computationally expensive: Training and using deep learning models can be computationally expensive. This can be a challenge for organizations with limited resources.