# How to handle certain types of anomalies in the input when using CNN

I am trying to perform binary prediction in a problem where the measurements come from objects that are spatially ordered as a grid and there is a physical meaning to the neighborhood on the grid. I thought to use convolutional networks and use a topology that reminds pixel segmentation where each object is like a pixel (i.e. that gives a label per object at the output). each type of measurement will be a distinct feature map

My questions are regarding how to handle the input features:
- assume the measurements are in the range [0,10] .
- The grid of object is not a rectangle. need to pad it. what's the most proper value to put on the margins ?
- Some of the measurements are missing. is there a common practice on how to fill in the missing data in this case ?
- Some of the measurements include outlier values (e.g. close to 100). what's the best way to handle outliers in this case ?

I would eventually want to use CNN so looking for most appropriate pre-processing techniques to handle the above for that model.

3. Set outlier values to 1, and then add on another channel with $\log(x)$ for all outlier $x$ values and $0$ otherwise. Make sure all the values are still within a reasonable range or renormalize.