  Research has shown that climate change creates warmer temperatures and drier
conditions, leading to longer wildfire seasons and increased wildfire risks in
the United States. These factors have in turn led to increases in the
frequency, extent, and severity of wildfires in recent years. Given the danger
posed by wildland fires to people, property, wildlife, and the environment,
there is an urgency to provide tools for effective wildfire management. Early
detection of wildfires is essential to minimizing potentially catastrophic
destruction. In this paper, we present our work on integrating multiple data
sources in SmokeyNet, a deep learning model using spatio-temporal information
to detect smoke from wildland fires. Camera image data is integrated with
weather sensor measurements and processed by SmokeyNet to create a multimodal
wildland fire smoke detection system. We present our results comparing
performance in terms of both accuracy and time-to-detection for multimodal data
vs. a single data source. With a time-to-detection of only a few minutes,
SmokeyNet can serve as an automated early notification system, providing a
useful tool in the fight against destructive wildfires.
