Abstract
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.
Abstract (translated)
从视觉数据中准确检测洪水是提高灾害应对和风险评估的关键步骤，然而由于收集和标注大规模图像的困难，用于洪水分割的数据集仍然稀缺。现有资源通常在地理范围和注释细节上受到限制，阻碍了稳健且通用的计算机视觉方法的发展。为了弥合这一差距，我们引入了AIFloodSense，这是一个全面、公开可用的航空影像数据集，包含来自64个国家和地区（六大洲）230次不同洪水事件的470张高分辨率图像。与之前的基准相比，AIFloodSense确保了全球多样性和时间相关性（2022-2024年），支持三项互补任务：(i) 图像分类，包括环境类型、拍摄角度和大陆识别的新子任务；(ii) 语义分割，提供洪水、天空和建筑物的精确像素级掩码；以及(iii) 视觉问答（VQA），以实现灾难评估中的自然语言推理。我们使用最先进的架构为所有任务建立了基准，并展示了该数据集的复杂性及其在推动气候适应性领域泛化人工智能工具发展方面的价值。
URL
https://arxiv.org/abs/2512.17432
