Abstract
Rapid and accurate structural damage assessment following natural disasters is critical for effective emergency response and recovery. However, remote sensing imagery often suffers from low spatial resolution, contextual ambiguity, and limited semantic interpretability, reducing the reliability of traditional detection pipelines. In this work, we propose a novel hybrid framework that integrates AI-based super-resolution, deep learning object detection, and Vision-Language Models (VLMs) for comprehensive post-disaster building damage assessment. First, we enhance pre- and post-disaster satellite imagery using a Video Restoration Transformer (VRT) to upscale images from 1024x1024 to 4096x4096 resolution, improving structural detail visibility. Next, a YOLOv11-based detector localizes buildings in pre-disaster imagery, and cropped building regions are analyzed using VLMs to semantically assess structural damage across four severity levels. To ensure robust evaluation in the absence of ground-truth captions, we employ CLIPScore for reference-free semantic alignment and introduce a multi-model VLM-as-a-Jury strategy to reduce individual model bias in safety-critical decision making. Experiments on subsets of the xBD dataset, including the Moore Tornado and Hurricane Matthew events, demonstrate that the proposed framework enhances the semantic interpretation of damaged buildings. In addition, our framework provides helpful recommendations to first responders for recovery based on damage analysis.
Abstract (translated)
自然灾害后快速准确的结构损伤评估对于有效的应急响应和恢复至关重要。然而，遥感影像常受限于空间分辨率低、上下文模糊以及语义可解释性不足，这降低了传统检测管线的可靠性。本研究提出一种新颖的混合框架，该框架整合了基于AI的超分辨率技术、深度学习目标检测以及视觉语言模型（VLMs），用于综合灾后建筑损伤评估。首先，我们采用视频恢复Transformer（VRT）对灾前灾后卫星影像进行增强，将图像从1024×1024分辨率提升至4096×4096，从而改善结构细节的可视性。随后，基于YOLOv11的检测器在灾前影像中定位建筑物，并对裁剪后的建筑区域使用VLMs进行语义分析，按四个严重程度等级评估结构损伤。为确保在缺乏真实标注描述的情况下进行稳健评估，我们采用CLIPScore进行无参考语义对齐，并引入多模型VLM-as-a-Jury策略以减少安全关键决策中的个体模型偏差。在xBD数据集子集（包括摩尔龙卷风和飓风马修事件）上的实验表明，所提框架增强了受损建筑的语义解释能力。此外，我们的框架还能基于损伤分析为救援人员提供恢复建议。
URL
https://arxiv.org/abs/2603.22768
