[2603.23020] Concept-based explanations of Segmentation and Detection models in Natural Disaster Management

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.23020 (cs) [Submitted on 24 Mar 2026] Title:Concept-based explanations of Segmentation and Detection models in Natural Disaster Management Authors:Samar Heydari, Jawher Said, Galip Ümit Yolcu, Evgenii Kortukov, Elena Golimblevskaia, Evgenios Vlachos, Vasileios Mygdalis, Ioannis Pitas, Sebastian Lapuschkin, Leila Arras View a PDF of the paper titled Concept-based explanations of Segmentation and Detection models in Natural Disaster Management, by Samar Heydari and 9 other authors View PDF HTML (experimental) Abstract:Deep learning models for flood and wildfire segmentation and object detection enable precise, real-time disaster localization when deployed on embedded drone platforms. However, in natural disaster management, the lack of transparency in their decision-making process hinders human trust required for emergency response. To address this, we present an explainability framework for understanding flood segmentation and car detection predictions on the widely used PIDNet and YOLO architectures. More specifically, we introduce a novel redistribution strategy that extends Layer-wise Relevance Propagation (LRP) explanations for sigmoid-gated element-wise fusion layers. This extension allows LRP relevances to flow through the fusion modules of PIDNet, covering the entire computation graph back to the input image. Furthermore, we apply Prototypical Concept-based Explanations (PCX) to provide both local ...