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

• URL: http://arxiv.org/abs/2603.23020v1
• Date: Tue, 24 Mar 2026 10:03:36 GMT
• Title: Concept-based explanations of Segmentation and Detection models in Natural Disaster Management
• Abstract summary: We present an explainability framework for understanding flood segmentation and car detection predictions on PIDNet and YOLO architectures.<n>Our framework provides reliable and interpretable explanations while maintaining near real-time inference capabilities.
• Score: 20.75438415554194
• License: http://creativecommons.org/licenses/by/4.0/
• 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 and global explanations at the concept level, revealing which learned features drive the segmentation and detection of specific disaster semantic classes. Experiments on a publicly available flood dataset show that our framework provides reliable and interpretable explanations while maintaining near real-time inference capabilities, rendering it suitable for deployment on resource-constrained platforms, such as Unmanned Aerial Vehicles (UAVs).

Related papers

• REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling [56.888597798431704]
  Open-World Object Detection (OWOD) requires detectors to identify previously unseen objects as unknown and incrementally incorporate them into the set of known categories.<n>Existing frameworks rely heavily on exemplar replay to mitigate catastrophic forgetting, but in some real applications, storing raw data conflicts with data access restrictions and leads to data exposure risks.<n>We propose REAL-OW, a novel rehearsal-free framework that decouples incremental knowledge through a collaborative adapter architecture.
  arXiv  Detail & Related papers  (2026-07-03T06:38:54Z)
• ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding [26.48338017389645]
  ChangeQuery is a unified multimodal framework designed for comprehensive, all-weather disaster situation awareness.<n>It is trained on structured data to provide precise damage quantification, region-specific descriptions, and holistic post-disaster summaries.<n>Experiments demonstrate that ChangeQuery establishes a new state-of-the-art, providing a robust and interpretable solution for complex disaster monitoring.
  arXiv  Detail & Related papers  (2026-04-24T08:05:39Z)
• Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization [56.95448807869383]
  Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning.<n>Recent studies find that the gradients exchanged in the FL system are also vulnerable to privacy leakage.<n>We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD)
  arXiv  Detail & Related papers  (2026-04-01T14:32:15Z)
• Beyond detection: cooperative multi-agent reasoning for rapid onboard EO crisis response [39.146761527401424]
  Rapid identification of hazardous events is essential for next-generation Earth Observation missions.<n>Current monitoring pipelines are largely ground-centric, introducing latency due to downlink limitations, multi-source data fusion constraints, and the computational cost of exhaustive scene analysis.<n>This work proposes a hierarchical multi-agent architecture for onboard EO processing under strict resource and bandwidth constraints.
  arXiv  Detail & Related papers  (2026-03-20T11:20:03Z)
• Vision-Language Feature Alignment for Road Anomaly Segmentation [38.2615882515309]
  We propose a vision-language anomaly segmentation framework that incorporates semantic priors from pre-trained Vision-Language Models (VLMs)<n>Specifically, we design a prompt learning-driven alignment module that adapts Mask2Forme's visual features to CLIP text embeddings of known categories.<n>At inference time, we introduce a multi-source inference strategy that integrates text-guided similarity, CLIP-based image-text similarity and detector confidence.
  arXiv  Detail & Related papers  (2026-03-01T10:17:00Z)
• Mechanistic Analysis of Circuit Preservation in Federated Learning [0.3823356975862005]
  Federated Learning (FL) enables collaborative training of models on decentralized data, but its performance degrades significantly under Non-IID data conditions.<n>This paper investigates the canonical FedAvg algorithm through the lens of Mechanistic Interpretability (MI) to diagnose this failure mode.
  arXiv  Detail & Related papers  (2025-12-28T19:03:14Z)
• Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learning [13.839214658191038]
  We propose Structural Information-based Hierarchical Diffusion framework for effective and stable offline policy learning.<n>We analyze structural information embedded in offline trajectories to construct the diffusion hierarchy adaptively.<n>We show that SIHD significantly outperforms state-of-the-art baselines in decision-making performance.
  arXiv  Detail & Related papers  (2025-09-26T06:24:06Z)
• RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation [51.37553739930992]
  RPCANet++ is a sparse object segmentation framework that fuses the interpretability of RPCA with efficient deep architectures.<n>Our approach unfolds a relaxed RPCA model into a structured network comprising a Background Approximation Module (BAM), an Object Extraction Module (OEM) and an Image Restoration Module (IRM)<n>Experiments on diverse datasets demonstrate that RPCANet++ achieves state-of-the-art performance under various imaging scenarios.
  arXiv  Detail & Related papers  (2025-08-06T08:19:37Z)
• Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning [83.41487567765871]
  Skipper is a model-based reinforcement learning framework. It automatically generalizes the task given into smaller, more manageable subtasks. It enables sparse decision-making and focused abstractions on the relevant parts of the environment.
  arXiv  Detail & Related papers  (2023-09-30T02:25:18Z)
• GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization [52.55628139825667]
  Federated Learning (FL) has emerged as a promising distributed machine learning framework to preserve clients' privacy. Recent studies find that an attacker can invert the shared gradients and recover sensitive data against an FL system by leveraging pre-trained generative adversarial networks (GAN) as prior knowledge. We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD), which disassembles the GAN model and searches the feature domains of the intermediate layers.
  arXiv  Detail & Related papers  (2023-08-09T04:34:21Z)
• Change Detection for Local Explainability in Evolving Data Streams [72.4816340552763]
  Local feature attribution methods have become a popular technique for post-hoc and model-agnostic explanations. It is often unclear how local attributions behave in realistic, constantly evolving settings such as streaming and online applications. We present CDLEEDS, a flexible and model-agnostic framework for detecting local change and concept drift.
  arXiv  Detail & Related papers  (2022-09-06T18:38:34Z)
• Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection [50.53262868498824]
  Shape deformation of targets in SAR image due to random orientation and partial information loss is an essential challenge in SAR ship detection. We propose a data augmentation method to train a deep network that is robust to partial information loss within the targets.
  arXiv  Detail & Related papers  (2022-02-14T07:01:01Z)
• Exploiting latent representation of sparse semantic layers for improved short-term motion prediction with Capsule Networks [0.12183405753834559]
  This paper explores use of Capsule Networks (CapsNets) in the context of learning a hierarchical representation of sparse semantic layers corresponding to small regions of the High-Definition (HD) map. By using an architecture based on CapsNets the model is able to retain hierarchical relationships between detected features within images whilst also preventing loss of spatial data often caused by the pooling operation. We show that our model achieves significant improvement over recently published works on prediction, whilst drastically reducing the overall size of the network.
  arXiv  Detail & Related papers  (2021-03-02T11:13:43Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.