Hey, so we are students trying to figure out how to do Deep Learning stuff through ArcGIS Pro and we have some questions before we commit to our workflow. I've gone down the rabbit hole for awhile and there are a few things I just cannot find explanations for.
1. We are using some raster tiff satellite imagery that has pyramids that clearly change the pixel resolution when you zoom in and out. We are planning to do object localization with airplanes, where we will draw polygons around airplanes. Does it matter what scale we are at when we export the training data (creating image chips) if the rasterization changes at different scales?
2. How do we combine training data sets? Let's say we have like a dozen images we are making training samples from, and we have to export our training samples for each image, creating a separate image chip directory each time. What is the best method for combining all of these image chips into a single directory that can be used once for the training of the deep learning model?
I am aware that we can add a pre-existing .dlpk file in the Train Deep Learning Model tool, but this seems very inefficient to do multiple times. Also we can't just manually copy and paste the subfiles into the same directories because of their numbering schemes and whatnot. There must be a better way!
3. Also I just want to confirm if the processes in ArcGIS Pro Imagery Analyst toolset automatically augment the training data (rotate, flip, translate, etc.). I assume that this is why it forces us to choose a neural network (like ResNet) during the training of the model, but I have not seen 100% confirmation on this.
