With the increasing occurrence of wildfires globally, quick and
effective detection methods are vital. This paper proposes an innovative
solution for wildfire detection using Unmanned Aerial Vehicle
(UAV)-assisted detection systems. On the other hand, semantic
communication, a technology designed for efficient data transmission in
specialized tasks, plays a crucial role in next-generation wireless
communications systems. In this paper, the deep joint source-channel
coding (DJSCC) scheme has been used for efficient image transmission as
a deep learning-based semantic communication technique for wildfire
detection. DJSCC improves source and channel coding for semantic
communications, offering advantages such as improved energy efficiency,
reduced latency, and improved reliability compared to traditional source
and channel code schemes. In this paper, the transmitter-receiver
operations of the UAV communication system are modeled as a DJSCC, and
they are jointly trained while taking into account the effects of the
fading channel. The encoder transforms captured images into compact
feature vectors, subsequently transmitting them using a reduced number
of channels to minimize latency. Rather than engaging in the
reconstruction of the input image in the receiver, the classifier
performs a classification task using the received signals at the
receiver. Alternatively, if the recovery of an image is required to
understand the spread of the wildfire, the decoder reconstructs it by
using the received signal at the receiver.