Suburban Forest Fire Risk Assessment and Forest Surveillance Using 360-Degree Cameras and a Multiscale Deformable Transformer

Abstract

In the current context of climate change and demographic expansion, one of the phenomena that humanity faces are the suburban wildfires. To prevent the occurrence of suburban forest fires, fire risk assessment and early fire detection approaches need to be applied. Forest fire risk mapping depends on various factors and contributes to the identification and monitoring of vulnerable zones where risk factors are most severe. Therefore, watchtowers, sensors, and base stations of autonomous unmanned aerial vehicles need to be placed carefully in order to ensure adequate visibility or battery autonomy. In this study, fire risk assessment of an urban forest was performed and the recently introduced 360-degree data were used for early fire detection. Furthermore, a single-step approach that integrates a multiscale vision transformer was introduced for accurate fire detection. The study area includes the suburban pine forest of Thessaloniki city (Greece) named Seich Sou, which is prone to wildfires. For the evaluation of the performance of the proposed workflow, real and synthetic 360-degree images were used. Experimental results demonstrate the great potential of the proposed system, which achieved an F-score for real fire event detection rate equal to 91.6%. This indicates that the proposed method could significantly contribute to the monitoring, protection, and early fire detection of the suburban forest of Thessaloniki.

Keywords

• suburban forest fire
• forest fire risk assessment
• early fire detection
• deep learning
• deformable transformer
• 360-degree cameras