takara-ai/FloodNet_2021-Track_2_Dataset_HF
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--- license: cc-by-sa-4.0 task_categories: - visual-question-answering language: - en size_categories: - 1K<n<10K --- # FloodNet: High Resolution Aerial Imagery Dataset for Post-Flood Scene Understanding This is the HF-hosted version of **FloodNet**.  The **FloodNet 2021: A High Resolution Aerial Imagery Dataset for Post-Flood Scene Understanding** provides high-resolution UAS imageries with detailed semantic annotation regarding the damages. To advance the damage assessment process for post-disaster scenarios, the authors of the dataset presented a unique challenge considering **classification**, **semantic segmentation**, and **visual question answering (VQA)**, highlighting the UAS imagery-based FloodNet dataset. ## Challenge Tracks The Challenge has two tracks: 1. **Image Classification and Semantic Segmentation** (available on [DatasetNinja](https://datasetninja.com/floodnet-track-2)) 2. **Visual Question Answering** (current) --- Frequent, and increasingly severe, natural disasters threaten human health, infrastructure, and natural systems. The provision of accurate, timely, and understandable information has the potential to revolutionize disaster management. For quick response and recovery on a large scale, after a natural disaster such as a hurricane, access to aerial images is critically important for the response team. The emergence of small **unmanned aerial systems (UAS)**, along with inexpensive sensors, presents the opportunity to collect thousands of images after each natural disaster with high flexibility and easy maneuverability for rapid response and recovery. Moreover, UAS can access hard-to-reach areas and perform data collection tasks that can be unsafe for humans if not impossible. Despite all these advancements and efforts to collect such large datasets, analyzing them and extracting meaningful information remains a significant challenge in scientific communities. --- ## Data Collection The data was collected with a small UAS platform, **DJI Mavic Pro quadcopters**, after Hurricane Harvey. The entire dataset contains **2343 images**, divided into: - **Training set (~60%)** - **Validation set (~20%)** - **Test set (~20%)** For **Track 1** (Semi-supervised Classification and Semantic Segmentation), in the training set, there are: - Around **400 labeled images** (~25% of the training set) - Around **1050 unlabeled images** (~75% of the training set) For **Track 2** (Supervised VQA), in the training set, there are: - Around **1450 images** - A total of **4511 image-question pairs** --- ## Annotations for Visual Question Answering (VQA) The presented dataset contains annotations from **Track 2**. For the **Visual Question Answering (VQA)** task, the images are associated with multiple questions. These questions are divided into the following categories: - **Simple Counting**: Questions will be designed to count the number of objects regardless of their attribute. _For example: "How many buildings are there in the image?"_ - **Complex Counting**: Questions will be asked to count the number of objects belonging to a specific attribute. _For example: "How many flooded buildings are there in the image?"_ - **Condition Recognition**: In this category, questions are mainly designed to ask about the condition of the object and the neighborhood. _For example: "What is the condition of the road in the given image?"_ - **Yes/No Questions**: For this type of question, the answer will be either ‘Yes’ or ‘No’. _For example: "Is there any flooded road?"_ --- ## Citation If you use FloodNet in your work, please cite the following paper: ```bibtex @misc{rahnemoonfar2020floodnet, title={FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding}, author={Maryam Rahnemoonfar and Tashnim Chowdhury and Argho Sarkar and Debvrat Varshney and Masoud Yari and Robin Murphy}, year={2020}, eprint={2012.02951}, archivePrefix={arXiv}, primaryClass={cs.CV}, doi={10.48550/arXiv.2012.02951} } ```
--- 许可证：知识共享署名-相同方式共享4.0（CC BY-SA 4.0） 任务类别： - 视觉问答 语言： - 英语 样本规模： - 1000 < 样本数量 < 10000 --- # FloodNet：面向灾后场景理解的高分辨率航空影像数据集 本仓库为**FloodNet**数据集在Hugging Face（HF）平台的托管版本。  **FloodNet 2021：面向灾后场景理解的高分辨率航空影像数据集**提供了带有损伤细节语义标注的高分辨率无人机航空影像。为推进灾后场景的损伤评估流程，该数据集的构建者针对**分类**、**语义分割**以及**视觉问答（VQA）**三大任务设计了专属挑战，重点突出了基于无人机系统（Unmanned Aerial Systems，UAS）航空影像的FloodNet数据集。 ## 挑战赛赛道 本次挑战赛共设两个赛道： 1. **图像分类与语义分割赛道**（可在[DatasetNinja平台](https://datasetninja.com/floodnet-track-2)获取） 2. **视觉问答赛道**（即当前赛道） --- 频发且愈发严峻的自然灾害正威胁着人类健康、基础设施与自然生态系统。精准、及时且易于理解的灾情信息有望彻底变革灾害管理模式。在飓风等自然灾害发生后，为实现大规模快速响应与灾后恢复，应急响应团队亟需获取航空影像。 小型**无人机系统（Unmanned Aerial Systems，UAS）**与低成本传感器的出现，使得灾后可灵活、便捷地采集数千张影像以支撑快速响应与恢复工作。此外，无人机系统能够抵达人类难以进入的区域，完成即便并非完全不可能、也会对人类构成安全风险的数据采集任务。尽管在这类大规模数据集的采集方面已取得诸多进展与实践，但对其进行分析并提取有价值的信息，仍是科学界面临的重大挑战。 --- ## 数据采集 本数据集于飓风哈维（Hurricane Harvey）灾后，通过小型无人机平台**大疆御Pro无人机（DJI Mavic Pro quadcopters）**采集。完整数据集共包含**2343张影像**，划分为： - 训练集（约60%） - 验证集（约20%） - 测试集（约20%） 针对**赛道1（半监督分类与语义分割）**，训练集包含： - 约**400张标注影像**（占训练集的25%） - 约**1050张未标注影像**（占训练集的75%） 针对**赛道2（有监督视觉问答）**，训练集包含： - 约**1450张影像** - 总计**4511组影像-问答对** --- ## 视觉问答（VQA）任务标注 本数据集包含**赛道2**的标注内容。针对**视觉问答（VQA）**任务，每张影像均关联多个问题，这些问题可分为以下几类： - **简单计数类**：问题设计用于统计影像中物体的总数量，不限制物体属性。 _示例："该影像中共有多少栋建筑？"_ - **复杂计数类**：问题用于统计具有特定属性的物体数量。 _示例："该影像中共有多少栋被洪水淹没的建筑？"_ - **状态识别类**：此类问题主要用于询问物体及其周边环境的状态。 _示例："该影像中的道路处于何种状态？"_ - **是非判断题**：此类问题的答案仅为"是"或"否"。 _示例："影像中是否存在被洪水淹没的道路？"_ --- ## 引用声明 若您在研究工作中使用FloodNet数据集，请引用以下文献： bibtex @misc{rahnemoonfar2020floodnet, title={FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding}, author={Maryam Rahnemoonfar and Tashnim Chowdhury and Argho Sarkar and Debvrat Varshney and Masoud Yari and Robin Murphy}, year={2020}, eprint={2012.02951}, archivePrefix={arXiv}, primaryClass={cs.CV}, doi={10.48550/arXiv.2012.02951} }
