arXiv 2604.223332026-04-24cs.CVcs.AI

ChangeQuery: 从视觉检测到语义理解，推动自然灾害和人为灾害的遥感变化分析

ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding

School of Computer Science and Technology and the Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University（计算机科学与技术学院和教育部智能网络与网络安全重点实验室，西安交通大学）；State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences（遥感与数字地球国家重点实验室，航空信息研究所，中国科学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯托克研究所）；Lancaster Environment Centre, Lancaster University（兰卡斯特大学环境研究中心）；Faculty of Electrical and Computer Engineering, University of Iceland（冰岛大学电气与计算机工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）

Dongwei Sun, Jing Yao, Kan Wei, Xiangyong Cao, Chen Wu, Zhenghui Zhao, Pedram Ghamisi, Jun Zhou, Jón Atli Benediktsson

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AI总结：

ChangeQuery通过多模态框架实现灾害情境感知，结合DICQ数据集和自动化标注管道，提升灾害监测的交互性和解释性，实现高精度的灾害量化与总结。

摘要：

在灾后响应中，快速的情报意识至关重要。尽管遥感损坏评估正从像素级变化检测发展到高级语义分析，现有视觉-语言方法仍难以为复杂战略查询提供可操作的智能。它们仍然受到单模态光学依赖、对自然灾害的偏见以及根本缺乏基础交互性的限制。为解决这些限制，我们提出了ChangeQuery，一个统一的多模态框架，用于全面、全天候的灾害情境感知。为克服模态限制和场景偏见，我们构建了灾害诱导变化查询（DICQ）数据集，一个大规模基准，将事前光学语义与事后SAR结构特征结合，涵盖平衡分布的自然灾害和武装冲突。此外，为提供交互推理所需的高质量监督，我们提出了一个新的自动化语义标注管道。遵循“统计优先，生成后”的范式，该引擎将原始分割掩码自动转换为扎根的、分层的指令集，有效地使模型具备细粒度空间和定量意识。在该结构化数据上训练的ChangeQuery架构运作为一个交互式灾害分析师。它支持由多样化用户查询驱动的多任务推理，提供精确的损坏量化、区域特定描述和整体灾后总结。广泛的实验表明，ChangeQuery建立了新的最先进的状态，提供了一个稳健且可解释的解决方案用于复杂灾害监测。代码可在https://sundongwei.github.io/changequery/获取。

英文摘要：

Rapid situational awareness is critical in post-disaster response. While remote sensing damage assessment is evolving from pixel-level change detection to high-level semantic analysis, existing vision-language methodologies still struggle to provide actionable intelligence for complex strategic queries. They remain severely constrained by unimodal optical dependence, a prevailing bias towards natural disasters, and a fundamental lack of grounded interactivity. To address these limitations, we present ChangeQuery, a unified multimodal framework designed for comprehensive, all-weather disaster situation awareness. To overcome modality constraints and scenario biases, we construct the Disaster-Induced Change Query (DICQ) dataset, a large-scale benchmark coupling pre-event optical semantics with post-event SAR structural features across a balanced distribution of natural catastrophes and armed conflicts. Furthermore, to provide the high-quality supervision required for interactive reasoning, we propose a novel Automated Semantic Annotation Pipeline. Adhering to a ``statistics-first, generation-later'' paradigm, this engine automatically transforms raw segmentation masks into grounded, hierarchical instruction sets, effectively equipping the model with fine-grained spatial and quantitative awareness. Trained on this structured data, the ChangeQuery architecture operates as an interactive disaster analyst. It supports multi-task reasoning driven by diverse user queries, delivering precise damage quantification, region-specific descriptions, and holistic post-disaster summaries. Extensive experiments demonstrate that ChangeQuery establishes a new state-of-the-art, providing a robust and interpretable solution for complex disaster monitoring. The code is available at \href{https://sundongwei.github.io/changequery/}{https://sundongwei.github.io/changequery/}.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。