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FloodNet.html
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<!doctype html>
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<title>Benchmark Datasets for Machine Learning for Natural Disasters</title>
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<h3><b>FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding</b></h3> <!-- Title -->
<h5> Image | Hurricane | Response </h5> <!-- Type | Topic | Disaster phase -->
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<div class="w3-container"> <!-- Data description -->
<p> FloodNet is dataset for post-flood scene understanding, i.e flood detection and distinguishing different water bodies and flood. It contains about 11, 000 question-image pairs for VQA and 3,200 images. It is introduced for image classification, semantic segmentation, and visual question answering.</p>
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<li class="list-group-item"><strong>ML task type:</strong> Image classification, semantic segmentation, visual question answering</li>
<li class="list-group-item"><strong>Data Source:</strong> Earth Observation Data and GeoSpatial Imagery (UAV)</li>
<li class="list-group-item"><strong>Size:</strong> ∼11, 000 Question-image pairs for VQA; 3,200 Images </li>
<li class="list-group-item"><strong>Timespan:</strong> August 30 - September 4, 2017</li>
<li class="list-group-item"> <strong>Geographical Coverage:</strong> Ford Bend County in Texas and other directly impacted areas from Hurricane Harvey </li>
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<li class="list-group-item"> <strong>Image Classification</strong></li>
<li class="list-group-item"> <strong>Evaluated on:</strong> InceptionNetv3, ResNet50, Xception</li>
<li class="list-group-item"> <strong>Metrics used:</strong> accuracy</li>
<li class="list-group-item"> <strong>Results as reported in original paper: </strong> Training Accuracy: 99.84% (Xception), Test Accuracy: 93.69% (ResNet50)</li>
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<li class="list-group-item"> <strong>Semantic Segementation</strong></li>
<li class="list-group-item"> <strong>Evaluated on:</strong> PSPNet, ENet,, DeepLabv3+ </li>
<li class="list-group-item"> <strong>Metrics used:</strong> Mean Intersection over Union</li>
<li class="list-group-item"> <strong>Results as reported in original paper: </strong> mIoU: 79.69 (PSPNet) </li>
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<li class="list-group-item"> <strong>Visual Question Answering</strong></li>
<li class="list-group-item"> <strong>Evaluated on:</strong> Concatenation of Features, Element-wise Multiplication of Features, SAN, MFB with Co-Attention </li>
<li class="list-group-item"> <strong>Metrics used:</strong> Overall Accuracy</li>
<li class="list-group-item"> <strong>Results as reported in original paper: </strong> Overall Accuracy: 0.73 (MFB with Co-Attention) </li>
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<li class="list-group-item"> <strong>Find out more by:</strong></li>
<li class="list-group-item"> <a href="https://ieeexplore.ieee.org/document/9460988" target="_blank">Reading the research paper</a></li>
<li class="list-group-item"> <a href="https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021" target="_blank">Checking out the dataset!</a></li>
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Maryam Rahnemoonfar, Tashnim Chowdhury, Argho Sarkar, Debvrat Varshney, Masoud Yari, and Robin Roberson
Murphy. FloodNet: A high resolution aerial imagery dataset for post flood scene understanding. IEEE Access, 9:89644–
89654, 2021.
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