Dataset Ninja LogoDataset Ninja:

FloodNet 2021: Track 2 Dataset

234801
Tagaerial, safety, environmental, drones
Taskidentification
Release YearMade in 2020
LicenseCC BY-SA 4.0
Download11 GB

Introduction #

Released 2020-12-05 ·Maryam Rahnemoonfar, Tashnim Chowdhury, Argho Sarkaret al.

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 highlighting the UAS imagery-based FloodNet dataset. The Challenge has two tracks: 1) Image Classification and Semantic Segmentation (available on DatasetNinja); and 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.

The data was collected with a small UAS platform, DJI Mavic Pro quadcopters, after Hurricane Harvey. The whole dataset has 2343 images, divided into training (~60%), validation (~20%), and test (~20%) sets.

For Track 1 (Semi-supervised Classification and Semantic Segmentation), in the training set, there are around 400 labeled images (~25% of the training set) and around 1050 unlabeled images (~75% of the training set). For Track 2 (Supervised VQA), in the training set, there are around 1450 images and there are a total of 4511 image-question pairs.

The presented dataset contains annotations from Track 2. For the Visual Question Answering (VQA) task, the images are associated with multiple questions. These questions will be 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, all the questions are mainly designed to ask questions regarding the condition of the object and the neighborhood. For example: “What is the condition of the road in the given image?”.
  • Yes/No type of question: For this type of question, the answer will be either a ‘Yes’ or a ‘No’. For example: “Is there any flooded road?”.
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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHub

Summary #

FloodNet 2021: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding (Track 2) is a dataset for an identification task. It is used in the search and rescue (SAR) and environmental industries.

The dataset consists of 2348 images with 0 labeled objects. There are 3 splits in the dataset: train_image (1448 images), test_image (450 images), and valid_image (450 images). Every image has its question tag. The dataset was released in 2020 by the UMBC, USA, Texas A&M University, and Dewberry, USA.

Dataset Poster

Explore #

FloodNet 2021: Track 2 dataset has 2348 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
OpenSample annotation mask from FloodNet 2021: Track 2Sample image from FloodNet 2021: Track 2
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Have a look at 2348 images
View images along with annotations and tags, search and filter by various parameters

License #

FloodNet 2021: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding (Track 2) is under CC BY-SA 4.0 license.

Source

Citation #

If you make use of the FloodNet 2021: Track 2 data, please cite the following reference:

@article{rahnemoonfar2020floodnet,
  title={FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding},
  author={Rahnemoonfar, Maryam and Chowdhury, Tashnim and Sarkar, Argho and Varshney, Debvrat and Yari, Masoud and Murphy, Robin},
  journal={arXiv preprint arXiv:2012.02951},
  year={2020}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-floodnet-track-dataset,
  title = { Visualization Tools for FloodNet 2021: Track 2 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/floodnet-track-2 } },
  url = { https://datasetninja.com/floodnet-track-2 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset FloodNet 2021: Track 2 can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='FloodNet 2021: Track 2', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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