构建全球洪水遥感数据集，助力灾情智能识别与评估
AIFloodSense: A Global Aerial Imagery Dataset for Semantic Segmentation and Understanding of Flooded Environments
- 覆盖64国、230次洪灾的470张高分辨率航拍图
- 支持分类、分割、问答三类任务，含像素级标注
- 适合灾害响应、遥感分析与跨域AI研究者使用
从视觉数据中精准检测洪水是提升灾后响应与风险评估的关键，但现有洪水分割数据集因采集和标注困难而稀缺。现有资源常受限于地理范围和标注细节，阻碍了鲁棒、泛化性强的计算机视觉方法发展。为此，我们推出AIFloodSense，一个公开可获取的综合性航拍影像数据集，包含来自64个国家、六大陆、230个不同洪水事件的470张高分辨率图像，时间跨度为2022–2024年。不同于以往基准，AIFloodSense确保全球多样性与时间相关性，支持三项互补任务：(i) 图像分类，新增环境类型、相机角度、大洲识别等子任务；(ii) 语义分割，提供洪水、天空、建筑物的像素级掩码；(iii) 视觉问答（VQA），支持自然语言推理以辅助灾情评估。我们采用先进模型建立各任务基线，验证数据集复杂性及其在推动气候韧性领域通用AI工具方面的价值。
原文摘要 · Abstract (English)
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.
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