Hello from Brazil, this is my first post around here!
I’m investigating possible deep learning problems for my undergraduate thesis in computer vision. So far satellite image segmentation seems somewhat manageable for a beginner.
I’m attempting to segment based on crop types as proved possible by this particular paper:
I also want my model to compute predictions as to how the segmentation is likely to be in the future, so as to potentially identify which areas are going to be rotated to which crops ahead of time. My plan is to make this information available through my university for general competitive analysis in the industry.
So far I have identified one paper covering spatiotemporal sequences. However, it employs an architecture that does not seem to be implemented in the fast.ai framework (conv-lstm).
I plan on using sentinel 2 data, which is freely available online
Here are my questions:
Is this feasible at my skill level? The little bit I know about deep learning came from this introductory course I managed to complete on time:
I’m also watching ‘deep learning for coders’ and general python skills are of course at a passable level.
What shortcomings should I be aware of before I start? Will I fail due to how big satellite imagery is?
Should I try another approach to this problem, such as a regular CNN instead of Conv-LSTM?
How many images do I need for my training and validation sets? Images sourced from google look all the same, should I look somewhere else?
I’m open to suggestions.
Thank you very much for your time.