MultiFire20K: A semi-supervised enhanced large-scale UAV-based benchmark for advancing multi-task learning in fire monitoring

Abstract

Effective fire detection and response are crucial to minimizing the widespread damage and loss caused by fires in both urban and natural environments. While advancements in Computer Vision have enhanced fire detection and response, progress in UAV-based monitoring remains limited due to the lack of comprehensive datasets. This study introduces the MultiFire20K dataset, comprising 20,500 diverse aerial fire images with annotations for fire classification, environment classification, and separate segmentation masks for both fire and smoke, specifically designed to support multi-task learning. Due to limited labeled data in remote sensing, a semi-supervised approach for generating pseudo-labels for fire and smoke masks is explored which takes into consideration the environment of the event. We experimented with various segmentation architectures backbone models to generate reliable pseudo-label masks. Benchmarks were established by evaluating models on fire classification, environment classification, and the segmentation of both fire and smoke, and comparing these results to those obtained from multi-task models. Our study highlights the substantial advantages of a multi-task approach in fire monitoring, particularly in improving fire and smoke segmentation through shared knowledge during training. This enhanced efficiency, combined with the conservation of memory and computational resources, makes the multi-task framework superior for real-time applications, especially when compared to using separate models for each individual task. We anticipate that our dataset and benchmark results will encourage further research in fire surveillance, advancing fire detection and prevention methods.