  In this short paper, we present our ongoing work on the veriFIRE project -- a
collaboration between industry and academia, aimed at using verification for
increasing the reliability of a real-world, safety-critical system. The system
we target is an airborne platform for wildfire detection, which incorporates
two deep neural networks. We describe the system and its properties of
interest, and discuss our attempts to verify the system's consistency, i.e.,
its ability to continue and correctly classify a given input, even if the
wildfire it describes increases in intensity. We regard this work as a step
towards the incorporation of academic-oriented verification tools into
real-world systems of interest.
