Purpose: 🖥️ Allows applying computations to the image data on GEE servers before downloading.
Typical use: 🤖 Ideal for deep learning workflows where pre-processing, such as normalization, cloud masking, or NDVI calculations, is needed directly on the server before downloading to reduce local computational load and data size.
Advantages:
Pre-processing on the server: 🛠️ Significantly reduces the amount of data to download by performing operations on the server (e.g., filtering, image enhancement).
Improved speed and efficiency: 🚀 Saves local processing time and resources by downloading pre-processed images, which is particularly beneficial for deep learning models that require specific input formats or preprocessing.
Optimized data handling: 📊 Minimizes bandwidth usage and optimizes data handling by transferring only the necessary processed data, making it ideal for scenarios with limited bandwidth or when handling large-scale datasets.