Firing Up Fire Detection Efforts with Deep Learning Models

Researchers at the San Diego Supercomputer Center (SDSC) at UC San Diego have developed new deep learning models to continue improving efforts for early wildfire detection. These efforts at SDSC are led by the WIFIRE Lab, which is an all-hazards knowledge cyberinfrastructure developed as a management layer from data collection to modeling. Recent research experiments integrated detailed on-the-ground, real-time camera footage, satellite-based fire detections and weather data to provide a multimodal approach to the early detection of wildfires.

Led by SDSC’s Lead for Data Analytics Mai H. Nguyen and UC San Diego Computer Science & Engineering Professor Garrison W. Cottrell, the team published their work in a paper titled Multimodal Wildland Fire Smoke Detection in Remote Sensing, MDPI.

The paper discusses the team’s utilization of deep learning models, which are artificial intelligence (AI) models that use multiple processing layers to learn representations of data at increasingly complex levels of abstraction. Using these representations, the model can detect patterns that can be used to make predictions. The models specifically presented in this research include the SmokeyNet baseline model, SmokeyNet Ensemble and Multimodal SmokeyNet extension. 

The baseline SmokeyNet is a spatiotemporal model consisting of three different deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Vision Transformer (ViT). The SmokeyNet Ensemble, designed to merge the SmokeyNet baseline model’s image-based smoke predictions with weather data and satellite-based fire predictions, computes a weighted average of fire predictions obtained from processing these three data sources. Meanwhile, the Multimodal SmokeyNet, an extension of the SmokeyNet baseline model, directly integrates weather data with camera images. 

“We use the Fire Ignition images Library (FIgLib) dataset, which consists of about 20,000 images across geographically distinct terrains throughout southern California, as the source for the camera data,” said Jaspreet Bhamra, first author on the paper and former machine learning intern at SDSC. “These images were processed by SmokeyNet to detect wildfire smoke. We also use information regarding weather near the location of the images from three weather stations closely neighboring the cameras.”