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The size and frequency of wildland fires in the western United States havedramatically increased in recent years. On high-fire-risk days, a small fireignition can rapidly grow and become out of control. Early detection of fireignitions from initial smoke can assist the response to such fires before theybecome difficult to manage. Past deep learning approaches for wildfire smokedetection have suffered from small or unreliable datasets that make itdifficult to extrapolate performance to real-world scenarios. In this work, wepresent the Fire Ignition Library (FIgLib), a publicly available dataset ofnearly 25,000 labeled wildfire smoke images as seen from fixed-view camerasdeployed in Southern California. We also introduce SmokeyNet, a novel deeplearning architecture using spatiotemporal information from camera imagery forreal-time wildfire smoke detection. When trained on the FIgLib dataset,SmokeyNet outperforms comparable baselines and rivals human performance. Wehope that the availability of the FIgLib dataset and the SmokeyNet architecturewill inspire further research into deep learning methods for wildfire smokedetection, leading to automated notification systems that reduce the time towildfire response.

FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection