  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 have
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.
