Location
Virginia Modeling, Analysis and Simulation Center, Room 1201
Conference Title
Modeling, Simulation and Visualization Student Capstone Conference 2023
Conference Track
General Sciences & Engineering
Document Type
Paper
Abstract
Satellite image analysis of natural disasters is critical for effective emergency response, relief planning, and disaster prevention. Semantic segmentation is believed to be on of the best techniques to capture pixelwise information in computer vision. In this work we will be using a U-Net architecture to do a three class semantic segmentation for the Xview2 dataset to capture the level of damage caused by different natural disaster which is beyond the visual scope of human eyes.
Keywords:
Natural disaster, Damage assessment, Semantic segmentation, U-Net
Start Date
4-20-2023
End Date
4-20-2023
Recommended Citation
Nipa, Nishat Ara, "U-Net Based Multiclass Semantic Segmentation for Natural Disaster Based Satellite Imagery" (2023). Modeling, Simulation and Visualization Student Capstone Conference. 2.
https://digitalcommons.odu.edu/msvcapstone/2023/sciencesandengineering/2
Included in
Computer Engineering Commons, Emergency and Disaster Management Commons, Theory and Algorithms Commons
U-Net Based Multiclass Semantic Segmentation for Natural Disaster Based Satellite Imagery
Virginia Modeling, Analysis and Simulation Center, Room 1201
Satellite image analysis of natural disasters is critical for effective emergency response, relief planning, and disaster prevention. Semantic segmentation is believed to be on of the best techniques to capture pixelwise information in computer vision. In this work we will be using a U-Net architecture to do a three class semantic segmentation for the Xview2 dataset to capture the level of damage caused by different natural disaster which is beyond the visual scope of human eyes.