ABSTRACT
With climate change on the rise, new challenges for Natural Disaster Management (NDM) arise, leading to rapid advancements in Deep Neural Networks (DNNs), specifically in wildfire scenarios. Forest fire detection and segmentation and burnt area segmentation are critical tasks that require DNNs to achieve precise decision-making in near real-time. Given the complex and dynamic conditions of wildfires, the majority of data is sourced from drone imagery, which facilitates more efficient detection and monitoring of fire behavior. Additionally, due to the spatial variability of fire, specific metrics like image-level mean Average Precision (ImAP) can yield better results, providing better insight into the capabilities of DNNs. 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.
LECTURER SHORT CV
Matthaios Dimitrios Tzimas obtained his B.Sc. in Electrical and computer Engineering in 2023. He is currently a research assistant in the Artificial Intelligence and Information Analysis Laboratory in the Department of Informatics at the Aristotle University of Thessaloniki. His research is centered on applying deep learning techniques to object detection and semantic segmentation in the realm of natural disaster management.
Real-Time-Image-Segmentation
VIDEO