FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

By Maryam Rahnemoonfar, Tashnim Chowdhury, Argho Sarkar, Debvrat Varshney, Masoud Yari, Robin R. Murphy. IEEE Access, 2020

447 citations recorded by the source corpus.

Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable for post disaster damage assessments. On the other hand, existing natural disaster datasets include mainly satellite imagery which has low spatial resolution and a high revisit period. Therefore, they do not have a scope to provide quick and efficient damage assessment tasks. Unmanned Aerial Vehicle (UAV) can effortlessly access difficult places during any disaster and collect high resolution imagery that is required for aforementioned tasks of computer vision. To address these issues we present a high resolution UAV imagery, FloodNet, captured after the hurricane Harvey. This dataset demonstrates the post flooded damages of the affected areas. The images are labeled pixel-wise for semantic segmentation task and questions are produced for the task of visual question answering. FloodNet poses several challenges including detection of flooded roads and buildings and distinguishing between natural water and flooded water. With the advancement of deep learning algorithms, we can analyze the impact of any disaster which can make a precise understanding of the affected areas. In this paper, we compare and contrast the performances of baseline methods for image classification, semantic segmentation, and visual question answering on our dataset. FloodNet dataset can be downloaded from here: https://github.com/BinaLab/FloodNet-Supervised_v1.0.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/af2550268c34fb0376a3f18f150bf4f9ce3023b9

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In Computer Vision and Pattern Recognition

Citations in the corpus

GeoChat:Grounded Large Vision-Language Model for Remote Sensing (2023, methodology): “NWPU-RESISC-45[5] 31.5k Floodnet[25] 4k Grounding inputs.”
EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain (2024, methodology): “The ViT model splits the input image into patch sequences, allowing models to learn image structures independently.”