I would like to detect street and sidewalk surface in a very detailed (0.075m/pix) USGS High Resolution Orthoimagery which basically means image segmentation with two classes. Places in question are residential areas similar to this one. I will download uncompressed raw imagery in GeoTIFF from USGS for the detection.
I read that neural networks can perform very good in image segmentation and I would like to try them. I am a developer by day so I can code but am a beginner to neural networks only knowing the basic principles about architecture, weighting and backpropagation etc. Is it possible to jump right in into my task or do I need to start with something simpler? I would prefer jumping right in if it can save time.
I skimmed though few papers dealing with similar thing and they seem quite complicated. Is there some simple way I can get started? I mean maybe an open source project in neural networks that deals with image segmentation that is similar to my task and I could make use of it?
I see neural networks need to be trained first and I am prepared to do manual segmentation first to have data for training. However, I have no idea about neural network design/architecture, how to design the layers, how many layers do I need etc. I also would like to use the fact that the network would learn some basics on how streets and sidewalks are built - that they are (not sure if my term is correct) "linear structures" which usually run many meters in length and may not even end in the image, also that sidewalks usually run alongside streets, streets have intersections etc.