Abstract: With climate change accelerating, new challenges in Natural Disaster Management (NDM) are driving significant advancements in Deep Neural Networks (DNNs), particularly for wildfire detection and segmentation. These tasks demand precise, near real-time decision-making to address the complex and dynamic nature of wildfires. Drone imagery, a primary data source, enables efficient detection and monitoring of fire behavior, though dense smoke often obscures visible light. To overcome this limitation, DNNs that integrate infrared data with RGB are essential for accurate detection. Metrics like image-level mean Average Precision (ImAP) provide valuable insights into fire detection model performance, while optimizing object detection losses further refines results. Computer vision methodologies can help boost results significantly by efficiently pre-processing images (e.g., HSV, RGBS). These concepts, in addition to the already powerful state-of-the-art DNNs (e.g., PIDnet, CNN I2I), can enable real-time DNN inference providing vital insight into NDM strategies. However, like wildfire detection, the broader NDM field faces a scarcity of labeled data. Thus, developing unsupervised fire segmentation is crucial, as it can pave the way for more adaptable architectures capable of addressing a wide range of new disaster scenarios with improved generalizability and impact.
Figure 1: Forest fire detection.
Figure 2: Forest fire segmentation.
Wildfire-Image-Analysis-v3.1