Which approach is most effective for processing a large dataset of satellite images to detect deforestation?

Implementing a distributed GPU-accelerated Convolutional Neural Network (CNN) for image segmentation is the most effective approach for processing a large dataset of satellite images to detect deforestation due to several key factors.

First, CNNs are specifically designed for image processing tasks and have proven to be highly effective in extracting spatial features from images. This capability is crucial for detecting complex patterns, such as those found in varying degrees of deforestation across different satellite images.

Second, utilizing GPUs significantly accelerates the training and inference phases of the model. Since satellite images can be large and numerous, the computational power of GPUs allows for faster processing and enables the handling of vast amounts of data in parallel. This scalability is particularly beneficial when dealing with large datasets where time efficiency is a concern.

Third, a distributed approach allows for the workload to be shared across multiple GPU units, further enhancing processing capabilities and enabling the system to manage even larger datasets effectively. This parallel processing ability ensures that the model can analyze high-resolution images more quickly and efficiently than traditional methods.

In contrast, manually reviewing images would be impractical for large datasets due to time constraints and the potential for human error. Using a CPU-based image processing library may not provide sufficient speed or efficiency compared to GPU solutions,