Multimodal Wildland Fire Smoke Detection

TL;DR

This paper introduces SmokeyNet, a multimodal deep learning system that combines camera images and weather data for rapid wildland fire smoke detection, significantly improving early warning capabilities.

Contribution

The paper presents a novel multimodal deep learning model that integrates visual and sensor data for faster and more accurate wildfire smoke detection.

Findings

SmokeyNet achieves detection within a few minutes.

Multimodal data improves accuracy over single-source methods.

System offers potential for early wildfire alerts.

Abstract

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.