  Autonomous surveillance unmanned aerial vehicles (UAVs) are deployed to
observe the streets of the city for any suspicious activities. This paper
utilizes surveillance UAVs for the purpose of detecting the presence of a fire
in the streets. An extensive database is collected from UAV surveillance
drones. With the aid of artificial intelligence (AI), fire stations can swiftly
identify the presence of a fire emerging in the neighborhood. Spatio-temporal
split learning is applied to this scenario to preserve privacy and globally
train a fire classification model. Fires are hazardous natural disasters that
can spread very quickly. Swift identification of fire is required to deploy
firefighters to the scene. In order to do this, strong communication between
the UAV and the central server where the deep learning process occurs is
required. Improving communication resilience is integral to enhancing a safe
experience on the roads. Therefore, this paper explores the adequate number of
clients and data ratios for split learning in this UAV setting, as well as the
required network infrastructure.
