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

Effective flood risk management relies on accurate forecasting, yet the "black box" nature of stateof-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions. While existing Explainable AI (XAI) methods offer local attributions, they fail to provide the verifiable, operationally meaningful causal narratives required by disaster response authorities. To address this societal challenge, we propose Context-Aware Concept Distillation (CACD), a framework developed in collaboration with domain experts to distill opaque LSTMs into interpretable, hydrology-aware surrogate models. We introduce an unsupervised pipeline to discover a "Hydrological Language" and a Residual Hypernetwork that dynamically modulates these concepts based on static basin characteristics. Evaluated on 5,203 basins globally, our model achieves high fidelity (Median NSE 0.70), significantly outperforming black-box baselines (e.g., Multi Layer Perceptrons) on unseen future data. By demonstrating that human-interpretable concepts are sufficient to reconstruct flood dynamics, this work balances AI accuracy with the transparency required for responsible environmental decision-making.

Existing object detectors typically make predictions in a black-box manner and struggle to simultaneously provide discriminative evidence for their predictions, which limits their deployment in safety-critical scenarios. To explain model predictions, existing post-hoc explanation methods mostly rely on gradient-based or perturbation-based operators. These methods not only introduce additional memory and computational overhead but also make it difficult to ensure that the generated explanations faithfully reflect the model's internal decision-making process. To address these limitations, we propose PIEDet, a prototype-driven intrinsically explainable object detection framework. PIEDet innovatively embeds class prototypes as explicit discriminative units into the classification branch of a one-stage detector, thereby improving detection performance while providing intrinsic interpretability. First, PIEDet constructs hierarchical class prototypes at different detection levels, enabling the model to learn scale-aware class-semantic representations. Second, we propose a prototype-driven feature learning method consisting of prototype regularization and a region-to-prototype matching loss. The former enhances the inter-class discriminability of the prototypes, while the latter encourages prototype responses to focus on object regions. Finally, we introduce a scale-aligned hierarchical prototype supervision mechanism that assigns scale-matched supervision signals to different detection levels, thereby enhancing the scale specificity of the hierarchical prototypes. On the ExDark, RTTS, and VOC2012-FOG datasets, PIEDet improves [email protected] over the baseline by 4.7%, 1.6%, and 4.8%, respectively, while demonstrating superior computational efficiency. Compared with mainstream post-hoc explanation methods, PIEDet achieves a better balance between explanation quality and explanation cost.

With the increasing deployment of deep neural networks in critical systems, such as medical diagnostics and autonomous vehicles, ensuring their interpretability is crucial to building trust in decision-making systems. In the field of explainable artificial intelligence, prototype-based reasoning has gained particular popularity, as it mimics human cognitive processes by explaining model decisions based on visual similarity under the looks like this paradigm. While this paradigm has been thoroughly investigated in the context of global image classification, the interpretability of dense predictions, particularly semantic segmentation, remains largely unexplored despite its immense importance in tasks requiring precise object localization. Existing prototype-based interpretable segmentation models rely on ante-hoc architectures, which entails significant limitations because they require costly training from scratch and modifications to the network structure, ultimately leading to a noticeable drop in predictive performance compared to standard black-box models. To address this issue, we propose PiPS (Post-hoc interpretable Prototypical Segmentation), the first fully post-hoc solution for generating prototypical explanations for semantic segmentation models. Our method enables the extraction of intuitive, spatially localized explanations from any pre-trained network without modification or fine-tuning, thereby preserving 100% of the model's original predictive performance. This approach opens a new avenue for the safe and cost-effective deployment of transparent systems in advanced computer vision tasks. Codebase available at https://github.com/gmum/PIPS.