Is binary classification using CNN possible if the training data only consists of one class? Is binary classification using CNN possible if the training data only consists of one class?
I am working on landslide risk assessment using Convolutional Neural Networks and I want to train a network that can recognize high-risk areas using multi-spectral imagery. The bands will contain numeric and categorical data that I have found to be related to my field of work.
The problem is that I only have historical data indicating where a landslide has happened before and defining zones as low-risk is not reliable in this field (since we are not yet sure how these variables affect the risk or susceptibility, and I don't want to bias my categorization) and my training data will be made up of only one class.
Can this be done? Is training a network from scratch using only one class of training data possible?
If so, after building this network, can I use it to classify any zone and get any meaningful data from its output for risk assessment (for example, output value &quot;1&quot; being &quot;similar to past landslides&quot; and &quot;0&quot; being &quot;not similar at all&quot;)?
It probably won't work because of in the training of the artificial intelligence, it'll set the weights to always answer class 1, and your data will say you're right, and it'll continue forever.