AIFloodSense：一个全球空中影像数据集，用于洪水环境的语义分割与理解
AIFloodSense: A Global Aerial Imagery Dataset for Semantic Segmentation and Understanding of Flooded Environments
AI总结：
本文提出AIFloodSense数据集，包含470张高分辨率图像，覆盖64个国家和六个大洲，支持图像分类、语义分割和视觉问答三个任务，旨在提升洪水环境的灾害评估能力。
摘要：
准确从视觉数据中检测洪水是改进灾害响应和风险评估的关键步骤，但现有洪水分割数据集稀缺，因大规模影像的收集和标注具有挑战性。现有资源在地理范围和标注细节上有限，阻碍了鲁棒、通用的计算机视觉方法的发展。为填补这一差距，我们引入AIFloodSense，一个全面、公开的空中影像数据集，包含来自230个不同洪水事件的470张高分辨率图像，覆盖64个国家和六个大洲。与先前基准不同，AIFloodSense确保全球多样性和时间相关性（2022-2024），支持三个互补任务：（i）图像分类，包含环境类型、相机角度和大陆识别的新型子任务；（ii）语义分割提供精确的像素级掩膜用于洪水、天空和建筑物；（iii）视觉问答（VQA）以实现灾难评估的自然语言推理。我们使用最先进的架构建立所有任务的基线基准，证明了数据集的复杂性和其在推进通用人工智能工具以增强气候适应性方面的价值。
英文摘要：
Accurate flood detection from visual data is a critical step toward improving disaster response and risk assessment, yet datasets for flood segmentation remain scarce due to the challenges of collecting and annotating large-scale imagery. Existing resources are often limited in geographic scope and annotation detail, hindering the development of robust, generalized computer vision methods. To bridge this gap, we introduce AIFloodSense, a comprehensive, publicly available aerial imagery dataset comprising 470 high-resolution images from 230 distinct flood events across 64 countries and six continents. Unlike prior benchmarks, AIFloodSense ensures global diversity and temporal relevance (2022-2024), supporting three complementary tasks: (i) Image Classification with novel sub-tasks for environment type, camera angle, and continent recognition; (ii) Semantic Segmentation providing precise pixel-level masks for flood, sky, and buildings; and (iii) Visual Question Answering (VQA) to enable natural language reasoning for disaster assessment. We establish baseline benchmarks for all tasks using state-of-the-art architectures, demonstrating the dataset's complexity and its value in advancing domain-generalized AI tools for climate resilience.
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。
